# CUDA Device can't be detected on Jetson Orin Nano running JP7

**URL:** <https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847>\
**Category:** Jetson Orin Nano\
**Tags:** cuda\
**Created:** [September 21, 2026, 2:20am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847 "2026-09-21T02:20:02Z")\
**Posts on this page:** 20\
**Page:** 1

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [September 21, 2026, 2:20am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/1 "2026-09-21T02:20:02Z")

</div>

Hi, I recently saw that MiniCPM5 is released, so of course I wanted to try it out, but when I run it in Ollama, it was strangely slow, and I didn’t heard any coil whine from the GPU, so I ran `ollama ps` and this is the result:

```auto
NAME ID SIZE PROCESSOR CONTEXT UNTIL              
hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M 6263d7d0bd60 1.8 GB 100% CPU 4096 4 minutes from now  

```

It’s running completely on CPU… and this issue did not only happen on Ollama, other apps that uses CUDA have the same result, for example, LM Studio:

 ![image](https://global.discourse-cdn.com/nvidia/original/4X/b/3/0/b30cd69748a875ea9b419d005f44c1785113e345.png)  
but `nvidia-smi` can detect the GPU?

```auto
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.78 Driver Version: 595.78 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 Orin (nvgpu) N/A | N/A N/A | N/A |
| N/A N/A N/A N/A / N/A | Not Supported | N/A N/A |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+

```

I already tried reinstall the whole jetpack stack using apt but it didn’t do much…

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [September 21, 2026, 7:03am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/5 "2026-09-21T07:03:53Z")

</div>

Hi,

We run the model with the command shared below:  
(replace `huggingface-cli` with `hf`)

> <https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-ollama/SKILL.md>

Ollama can use 100% GPU as below:

```auto
$ ollama ps
NAME ID SIZE PROCESSOR CONTEXT UNTIL              
minicpm5-2b:latest 883ef2198ddf 1.9 GB 100% GPU 8192 3 minutes from now    
gemma:2b b50d6c999e59 2.1 GB 100% GPU 8192 2 minutes from now    

```

Thanks.

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [September 21, 2026, 7:06am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/6 "2026-09-21T07:06:51Z")

</div>

Hi, I ran the command you provided, but it still says 100% CPU

```auto
NAME ID SIZE PROCESSOR CONTEXT UNTIL              
hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M 6263d7d0bd60 1.8 GB 100% CPU 4096 4 minutes from now

```

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [September 30, 2026, 7:28am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/7 "2026-09-30T07:28:29Z")

</div>

Hi,

Could you set `OLLAMA_DEBUG=2` and share the output log with us?

```auto
$ sudo systemctl edit ollama.service

```

Add

```auto
[Service]
Environment="OLLAMA_DEBUG=2"
Environment="OLLAMA_IGPU_ENABLE=1"

```

Then run the model again and share the output of the command below:

```auto
$ journalctl -u ollama

```

Thanks.

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 4, 2026, 7:27am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/8 "2026-10-04T07:27:45Z")

</div>

Hi, here are the logs

```auto
 user   ~    export OLLAMA_DEBUG=2 OLLAMA_IGPU_ENABLE=1
 user   ~    ollama serve
time=2026-10-04T15:24:54.855+08:00 level=INFO source=routes.go:1947 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GGML_VK_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: LLAMA_ARG_FIT: LLAMA_ARG_FIT_TARGET: NO_PROXY: OLLAMA_CONTEXT_LENGTH:0 OLLAMA_DEBUG:DEBUG-4 OLLAMA_DEBUG_LOG_REQUESTS:false OLLAMA_EDITOR: OLLAMA_FLASH_ATTENTION:false OLLAMA_GO_TEMPLATE:true OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_IGPU_ENABLE:1 OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_TRANSFER_STREAMS:4 OLLAMA_MODELS:/home/user/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NO_CLOUD:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_REMOTES:[ollama.com] OLLAMA_SCHED_SPREAD:false OLLAMA_VULKAN:true ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]"
time=2026-10-04T15:24:54.856+08:00 level=INFO source=routes.go:1949 msg="Ollama cloud disabled: false"
time=2026-10-04T15:24:54.864+08:00 level=INFO source=images.go:883 msg="total blobs: 12"
time=2026-10-04T15:24:54.865+08:00 level=INFO source=images.go:890 msg="total unused blobs removed: 0"
time=2026-10-04T15:24:54.867+08:00 level=DEBUG source=model_recommendations.go:57 msg="starting model recommendations cache" default_recommendations=6 refresh_interval=4h0m0s fetch_timeout=3s
time=2026-10-04T15:24:54.867+08:00 level=DEBUG source=model_show_cache.go:128 msg="starting model show cache"
time=2026-10-04T15:24:54.867+08:00 level=DEBUG source=model_list_cache.go:71 msg="starting model list cache"
time=2026-10-04T15:24:54.867+08:00 level=INFO source=routes.go:2004 msg="Listening on [::]:11434 (version 0.32.0)"
time=2026-10-04T15:24:54.868+08:00 level=DEBUG source=sched.go:221 msg="starting llm scheduler"
time=2026-10-04T15:24:54.869+08:00 level=INFO source=runner.go:60 msg="discovering available GPUs..."
time=2026-10-04T15:24:54.869+08:00 level=DEBUG source=model_recommendations.go:262 msg="loaded model recommendations snapshot" path=/home/user/.ollama/cache/model-recommendations.json count=5
time=2026-10-04T15:24:54.869+08:00 level=DEBUG source=model_recommendations.go:192 msg="refreshing model recommendations from remote" url=https://ollama.com/api/experimental/model-recommendations
time=2026-10-04T15:24:54.870+08:00 level=DEBUG source=gpu.go:63 msg="unrecognized L4T version" nv_tegra_release="# R39 (release), REVISION: 2.1, GCID: 46758480, BOARD: generic, EABI: aarch64, DATE: Fri Aug 7 05:54:22 AM UTC 2026\n# KERNEL_VARIANT: oot\nTARGET_USERSPACE_LIB_DIR=nvidia\nTARGET_USERSPACE_LIB_DIR_PATH=usr/lib/aarch64-linux-gnu/nvidia\n"
time=2026-10-04T15:24:54.871+08:00 level=TRACE source=llama_server.go:71 msg="running llama-server for discovery" cmd=/usr/local/lib/ollama/llama-server libDirs="[/usr/local/lib/ollama /usr/local/lib/ollama/cuda_v13]"
time=2026-10-04T15:24:54.875+08:00 level=INFO source=model_list_cache.go:112 msg="model list cache hydration complete" models=5 failures=0 elapsed=7.685578ms
0.00.342.221 E ggml_cuda_init: failed to initialize CUDA: unknown error
0.00.345.575 I cmn common_param: common_params_print_info: build 1 (cb295bf59) with Clang 21.1.8 for Linux aarch64
0.00.345.584 I cmn common_param: common_params_print_info: verbosity = 2147483647 (adjust with the `-lv N` CLI arg)
0.00.347.038 I cmn common_param: system_info: n_threads = 6 (n_threads_batch = 6) / 6 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | DOTPROD = 1 | LLAMAFILE = 1 | REPACK = 1 | CUDA : ARCHS = 750,800,860,870,890,900,1000,1030,1100,1200,1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | 
time=2026-10-04T15:24:55.267+08:00 level=DEBUG source=llama_server.go:121 msg="llama-server discovery: stopped subprocess after collecting GPU info" exit=unknown libDirs="[/usr/local/lib/ollama /usr/local/lib/ollama/cuda_v13]"
0.00.165.746 E ggml_cuda_init: failed to initialize CUDA: unknown error
Available devices:
time=2026-10-04T15:24:55.728+08:00 level=DEBUG source=model_recommendations.go:225 msg="model recommendations refreshed" count=5
time=2026-10-04T15:24:55.734+08:00 level=DEBUG source=model_recommendations.go:302 msg="persisted model recommendations snapshot" path=/home/user/.ollama/cache/model-recommendations.json count=5
time=2026-10-04T15:24:55.734+08:00 level=INFO source=model_recommendations.go:177 msg="model recommendations cache sleep scheduled" wait=3h56m4.222731819s consecutive_failures=0
ggml_cuda_init: failed to initialize CUDA: unknown error
load_backend: loaded CUDA backend from /usr/local/lib/ollama/cuda_v13/libggml-cuda.so
Error: cuInit failed: 999
time=2026-10-04T15:24:55.856+08:00 level=DEBUG source=native_probe.go:259 msg="native GPU discovery failed" error="exit status 1" stderr="ggml_cuda_init: failed to initialize CUDA: unknown error\nload_backend: loaded CUDA backend from /usr/local/lib/ollama/cuda_v13/libggml-cuda.so\nError: cuInit failed: 999\n" libDirs="[/usr/local/lib/ollama /usr/local/lib/ollama/cuda_v13]"
time=2026-10-04T15:24:55.856+08:00 level=DEBUG source=llama_server.go:52 msg="llama-server device discovery took" duration=986.055231ms libDirs="[/usr/local/lib/ollama /usr/local/lib/ollama/cuda_v13]"
time=2026-10-04T15:24:55.857+08:00 level=DEBUG source=runner.go:494 msg="bootstrap discovery took" duration=986.815703ms OLLAMA_LIBRARY_PATH="[/usr/local/lib/ollama /usr/local/lib/ollama/cuda_v13]" extra_envs=map[]
time=2026-10-04T15:24:55.857+08:00 level=DEBUG source=runner.go:131 msg="evaluating which, if any, devices to filter out" initial_count=0
time=2026-10-04T15:24:55.857+08:00 level=TRACE source=runner.go:181 msg="supported GPU library combinations before filtering" supported=map[]
time=2026-10-04T15:24:55.858+08:00 level=DEBUG source=runner.go:42 msg="GPU bootstrap discovery took" duration=989.669581ms
time=2026-10-04T15:24:55.858+08:00 level=INFO source=types.go:50 msg="inference compute" id=cpu library=cpu compute="" name=cpu description=cpu libdirs=ollama driver="" pci_id="" type="" total="7.4 GiB" available="585.6 MiB"
time=2026-10-04T15:24:55.859+08:00 level=INFO source=routes.go:2054 msg="vram-based default context" total_vram="0 B" default_num_ctx=4096
[GIN] 2026/10/04 - 15:25:22 | 200 | 1.813207ms | 127.0.0.1 | HEAD "/"
[GIN] 2026/10/04 - 15:25:22 | 200 | 6.954228ms | 127.0.0.1 | GET "/api/tags"
[GIN] 2026/10/04 - 15:25:30 | 200 | 43.169µs | 127.0.0.1 | HEAD "/"
time=2026-10-04T15:25:30.667+08:00 level=TRACE source=ggml.go:327 msg="key with type not found" key=general.alignment default=32
[GIN] 2026/10/04 - 15:25:30 | 200 | 236.938178ms | 127.0.0.1 | POST "/api/show"
[GIN] 2026/10/04 - 15:25:30 | 200 | 2.522413ms | 127.0.0.1 | POST "/api/show"
time=2026-10-04T15:25:31.011+08:00 level=TRACE source=sched.go:247 msg="processing incoming request" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.012+08:00 level=TRACE source=sched.go:281 msg="refreshing GPU list" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.012+08:00 level=DEBUG source=runner.go:272 msg="refreshing free memory"
time=2026-10-04T15:25:31.012+08:00 level=DEBUG source=runner.go:42 msg="overall device VRAM discovery took" duration=385.868µs
time=2026-10-04T15:25:31.012+08:00 level=TRACE source=sched.go:284 msg="refreshing system information" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.012+08:00 level=TRACE source=gpu.go:22 msg="performing system memory discovery"
time=2026-10-04T15:25:31.012+08:00 level=TRACE source=gpu.go:25 msg="system memory discovery completed" duration=177.349µs
time=2026-10-04T15:25:31.012+08:00 level=DEBUG source=sched.go:296 msg="updating default concurrency" OLLAMA_MAX_LOADED_MODELS=3 gpu_count=0
time=2026-10-04T15:25:31.012+08:00 level=TRACE source=sched.go:300 msg="updating free space" gpu_count=0 model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.012+08:00 level=DEBUG source=sched.go:305 msg="loading first model" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.076+08:00 level=TRACE source=ggml.go:327 msg="key with type not found" key=general.alignment default=32
time=2026-10-04T15:25:31.077+08:00 level=INFO source=sched.go:1158 msg="disabling mmap for llama-server load by default" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb reason=cpu
time=2026-10-04T15:25:31.077+08:00 level=INFO source=server.go:109 msg="using llama-server for model" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.077+08:00 level=TRACE source=ggml.go:327 msg="key with type not found" key=llama.nextn_predict_layers default=0
time=2026-10-04T15:25:31.078+08:00 level=INFO source=llama_server.go:433 msg="starting llama-server" cmd="/usr/local/lib/ollama/llama-server --model /home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb --port 39025 --host 127.0.0.1 --no-webui --offline -c 4096 -np 1 --log-verbosity 4 --no-log-prefix --no-log-timestamps --no-mmap --flash-attn auto -b 512 -ub 512 --context-shift --keep 4"
time=2026-10-04T15:25:31.078+08:00 level=DEBUG source=llama_server.go:434 msg=subprocess GGML_BACKEND_PATH=/usr/local/lib/ollama/cuda_v13/libggml-cuda.so LD_LIBRARY_PATH=/usr/local/lib/ollama:/usr/local/lib/ollama/cuda_v13 PATH=/home/user/iOS/netmuxd/target/release:/home/user/KataGo/cpp:/home/user/Arduino-IDE:/home/user/iOS/netmuxd/target/release:/home/user/.opencode/bin:/home/user/.atuin/bin:/home/linuxbrew/.linuxbrew/bin:/home/linuxbrew/.linuxbrew/sbin:/opt/devkitpro/tools/bin:/home/user/.cargo/bin:/home/user/openpilot/.venv/bin:/home/user/.local/bin:/home/user/.nvm/versions/node/v24.12.0/bin:/usr/local/cuda-12.6/bin:/home/user/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/home/user/.local/bin:/home/user/.lmstudio/bin:/usr/local/go/bin:/home/user/go/bin
time=2026-10-04T15:25:31.079+08:00 level=INFO source=sched.go:623 msg="system memory" total="7.4 GiB" free="391.1 MiB" free_swap="21.3 GiB"
time=2026-10-04T15:25:31.079+08:00 level=INFO source=llama_server.go:1001 msg="loading model via llama-server" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:31.079+08:00 level=INFO source=llama_server.go:1248 msg="waiting for llama-server to start responding"
time=2026-10-04T15:25:31.080+08:00 level=INFO source=llama_server.go:1303 msg="waiting for llama-server to become available" status="llm server not responding"
0.00.355.251 E ggml_cuda_init: failed to initialize CUDA: unknown error
cmn common_param: common_params_print_info: build 1 (cb295bf59) with Clang 21.1.8 for Linux aarch64
cmn common_param: common_params_print_info: verbosity = 4 (adjust with the `-lv N` CLI arg)
cmn common_param: device_info:
cmn common_param: - CPU : CPU (7546 MiB, 7546 MiB free)
cmn common_param: system_info: n_threads = 6 (n_threads_batch = 6) / 6 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | DOTPROD = 1 | LLAMAFILE = 1 | REPACK = 1 | CUDA : ARCHS = 750,800,860,870,890,900,1000,1030,1100,1200,1210 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | 
srv init: using 5 threads for HTTP server
srv init: The UI is disabled
srv init: Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable
srv start: binding port with default address family
srv load_model: loading model '/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb'
srv load_model: local path '/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb'
cmn common_init_: fitting params to device memory ...
cmn common_init_: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
common_params_fit_impl: getting device memory data for initial parameters:
time=2026-10-04T15:25:31.586+08:00 level=INFO source=llama_server.go:1303 msg="waiting for llama-server to become available" status="llm server loading model"
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - Host | 368 = 144 + 168 + 56 |
common_memory_breakdown_print: | - CPU_REPACK | 1339 = 1339 + 0 + 0 |
common_params_fit_impl: projected to use 368 MiB of host memory vs. 7546 MiB of total host memory
common_params_fit_impl: will leave 7177 >= 1024 MiB of system memory, no changes needed
common_fit_params: successfully fit params to free device memory
common_fit_params: fitting params to free memory took 0.55 seconds
llama_model_loader: loaded meta data with 76 key-value pairs and 381 tensors from /home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 3: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 4: general.name str = MiniCPM5 2B Heretic Abliterated
llama_model_loader: - kv 5: general.finetune str = heretic-abliterated
llama_model_loader: - kv 6: general.basename str = MiniCPM5
llama_model_loader: - kv 7: general.size_label str = 2B
llama_model_loader: - kv 8: general.license str = apache-2.0
llama_model_loader: - kv 9: general.dataset.count u32 = 8
llama_model_loader: - kv 10: general.dataset.0.name str = Ultra FineWeb
llama_model_loader: - kv 11: general.dataset.0.organization str = Openbmb
llama_model_loader: - kv 12: general.dataset.0.repo_url str = https://huggingface.co/openbmb/Ultra-...
llama_model_loader: - kv 13: general.dataset.1.name str = UltraX Preview
llama_model_loader: - kv 14: general.dataset.1.organization str = Openbmb
llama_model_loader: - kv 15: general.dataset.1.repo_url str = https://huggingface.co/openbmb/UltraX...
llama_model_loader: - kv 16: general.dataset.2.name str = Ultra FineWeb L3
llama_model_loader: - kv 17: general.dataset.2.organization str = Openbmb
llama_model_loader: - kv 18: general.dataset.2.repo_url str = https://huggingface.co/openbmb/Ultra-...
llama_model_loader: - kv 19: general.dataset.3.name str = UltraData Math
llama_model_loader: - kv 20: general.dataset.3.organization str = Openbmb
llama_model_loader: - kv 21: general.dataset.3.repo_url str = https://huggingface.co/openbmb/UltraD...
llama_model_loader: - kv 22: general.dataset.4.name str = UltraData Code
llama_model_loader: - kv 23: general.dataset.4.organization str = Openbmb
llama_model_loader: - kv 24: general.dataset.4.repo_url str = https://huggingface.co/openbmb/UltraD...
llama_model_loader: - kv 25: general.dataset.5.name str = UltraData SFT 2605
llama_model_loader: - kv 26: general.dataset.5.version str = 2605
llama_model_loader: - kv 27: general.dataset.5.organization str = Openbmb
llama_model_loader: - kv 28: general.dataset.5.repo_url str = https://huggingface.co/openbmb/UltraD...
llama_model_loader: - kv 29: general.dataset.6.name str = UltraData SFT Agent 2609
llama_model_loader: - kv 30: general.dataset.6.version str = 2609
llama_model_loader: - kv 31: general.dataset.6.organization str = Openbmb
llama_model_loader: - kv 32: general.dataset.6.repo_url str = https://huggingface.co/openbmb/UltraD...
llama_model_loader: - kv 33: general.dataset.7.name str = UltraData RL 2609
llama_model_loader: - kv 34: general.dataset.7.version str = 2609
llama_model_loader: - kv 35: general.dataset.7.organization str = Openbmb
llama_model_loader: - kv 36: general.dataset.7.repo_url str = https://huggingface.co/openbmb/UltraD...
llama_model_loader: - kv 37: general.tags arr[str,14] = ["minicpm", "minicpm5", "llama", "tex...
llama_model_loader: - kv 38: general.languages arr[str,2] = ["en", "zh"]
llama_model_loader: - kv 39: llama.block_count u32 = 42
llama_model_loader: - kv 40: llama.context_length u32 = 131072
llama_model_loader: - kv 41: llama.embedding_length u32 = 2048
llama_model_loader: - kv 42: llama.feed_forward_length u32 = 6144
llama_model_loader: - kv 43: llama.attention.head_count u32 = 16
llama_model_loader: - kv 44: llama.attention.head_count_kv u32 = 2
llama_model_loader: - kv 45: llama.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 46: llama.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 47: llama.attention.key_length u32 = 128
llama_model_loader: - kv 48: llama.attention.value_length u32 = 128
llama_model_loader: - kv 49: llama.vocab_size u32 = 130560
llama_model_loader: - kv 50: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 51: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 52: tokenizer.ggml.pre str = minicpm5
llama_model_loader: - kv 53: tokenizer.ggml.tokens arr[str,130560] = ["<s>", "</s>", "<tool_call>", "</too...
llama_model_loader: - kv 54: tokenizer.ggml.token_type arr[i32,130560] = [3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, ...
llama_model_loader: - kv 55: tokenizer.ggml.merges arr[str,129794] = ["Ġ t", "h e", "Ġ a", "i n", "Ġ Ġ...
llama_model_loader: - kv 56: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 57: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 58: tokenizer.ggml.unknown_token_id u32 = 130074
llama_model_loader: - kv 59: tokenizer.ggml.padding_token_id u32 = 1
llama_model_loader: - kv 60: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 61: tokenizer.ggml.add_sep_token bool = false
llama_model_loader: - kv 62: tokenizer.chat_template str = {{- bos_token }}{%- if tools %}\n {...
llama_model_loader: - kv 63: general.quantization_version u32 = 2
llama_model_loader: - kv 64: general.file_type u32 = 15
llama_model_loader: - kv 65: general.url str = https://huggingface.co/mradermacher/M...
llama_model_loader: - kv 66: mradermacher.quantize_version str = 2
llama_model_loader: - kv 67: mradermacher.quantized_by str = mradermacher
llama_model_loader: - kv 68: mradermacher.quantized_at str = 2026-09-08T16:43:47+02:00
llama_model_loader: - kv 69: mradermacher.quantized_on str = back
llama_model_loader: - kv 70: general.source.url str = https://huggingface.co/insraq/MiniCPM...
llama_model_loader: - kv 71: mradermacher.convert_type str = hf
llama_model_loader: - kv 72: quantize.imatrix.file str = MiniCPM5-2B-heretic-abliterated-i1-GG...
llama_model_loader: - kv 73: quantize.imatrix.dataset str = imatrix-training-full-3
llama_model_loader: - kv 74: quantize.imatrix.entries_count u32 = 294
llama_model_loader: - kv 75: quantize.imatrix.chunks_count u32 = 312
llama_model_loader: - type f32: 85 tensors
llama_model_loader: - type q4_K: 253 tensors
llama_model_loader: - type q6_K: 43 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 1.45 GiB (4.95 BPW) 
load: 478 unused tokens
load: printing all EOG tokens:
load: - 1 ('</s>')
load: - 130073 ('<|im_end|>')
load: special tokens cache size = 510
load: token to piece cache size = 0.8291 MB
print_info: arch = llama
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 131072
print_info: n_embd_inp = 2048
print_info: n_embd = 2048
print_info: n_embd_out = 2048
print_info: n_layer = 42
print_info: n_layer_all = 42
print_info: n_head = 16
print_info: n_head_kv = 2
print_info: n_rot = 128
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 8
print_info: n_embd_k_gqa = 256
print_info: n_embd_v_gqa = 256
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: f_attn_value_scale = 0.0000
print_info: n_ff = 6144
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 0
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 131072
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: model type = ?B
print_info: model params = 2.52 B
print_info: general.name = MiniCPM5 2B Heretic Abliterated
print_info: vocab type = BPE
print_info: n_vocab = 130560
print_info: n_merges = 129794
print_info: BOS token = 0 '<s>'
print_info: EOS token = 1 '</s>'
print_info: EOT token = 130073 '<|im_end|>'
print_info: UNK token = 130074 '<unk>'
print_info: PAD token = 1 '</s>'
print_info: LF token = 220 'Ċ'
print_info: FIM PRE token = 5 '<|fim_prefix|>'
print_info: FIM SUF token = 7 '<|fim_suffix|>'
print_info: FIM MID token = 6 '<|fim_middle|>'
print_info: EOG token = 1 '</s>'
print_info: EOG token = 130073 '<|im_end|>'
print_info: max token length = 1024
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
load_tensors: CPU model buffer size = 144.10 MiB
load_tensors: CPU_REPACK model buffer size = 1339.98 MiB
cmn common_init_: added </s> logit bias = -inf
cmn common_init_: added <|im_end|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 4096
llama_context: n_ctx_seq = 4096
llama_context: n_batch = 512
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 5000000.0
llama_context: freq_scale = 1
llama_context: n_rs_seq = 0
llama_context: n_outputs_max = 1
llama_context: n_ctx_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context: CPU output buffer size = 0.50 MiB
llama_kv_cache: CPU KV buffer size = 168.00 MiB
llama_kv_cache: size = 168.00 MiB ( 4096 cells, 42 layers, 1/1 seqs), K (f16): 84.00 MiB, V (f16): 84.00 MiB
llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 128
llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 128
sched_reserve: reserving ...
sched_reserve: Flash Attention was auto, set to enabled
sched_reserve: resolving fused Gated Delta Net support:
sched_reserve: fused Gated Delta Net (autoregressive) enabled
sched_reserve: fused Gated Delta Net (chunked) enabled
sched_reserve: CPU compute buffer size = 56.01 MiB
sched_reserve: graph nodes = 1308
sched_reserve: graph splits = 1
sched_reserve: reserve took 62.74 ms, sched copies = 1
cmn common_init_: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv load_model: initializing, n_slots = 1, n_ctx_slot = 4096, kv_unified = 'false'
spec common_specu: no implementations specified for speculative decoding
slot load_model: id 0 | task -1 | new slot, n_ctx = 4096
srv load_model: prompt cache is enabled, size limit: 8192 MiB
srv load_model: use `--cache-ram 0` to disable the prompt cache
srv load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
srv load_model: context checkpoints enabled, max = 32, min spacing = 8192
srv init: idle slots will be saved to prompt cache upon starting a new task
srv init: init: chat template, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
<think>

</think>

Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
<think>
'
srv init: init: chat template, thinking = 1
srv init: chat template supports preserving reasoning, consider enabling it via --reasoning-preserve
srv llama_server: model loaded
srv llama_server: listening on http://127.0.0.1:39025
srv update_slots: all slots are idle
time=2026-10-04T15:25:52.560+08:00 level=INFO source=llama_server.go:1315 msg="llama-server started in 21.47 seconds"
time=2026-10-04T15:25:52.936+08:00 level=INFO source=images.go:373 msg="template selection" model=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M selected=gguf_chat_template renderer="" parser="" go_template=[completion] chat_template="[tools thinking completion]" harmony=null renderer_parser=null
time=2026-10-04T15:25:52.938+08:00 level=INFO source=sched.go:739 msg="loaded runners" count=1
time=2026-10-04T15:25:52.940+08:00 level=INFO source=llama_server.go:1248 msg="waiting for llama-server to start responding"
time=2026-10-04T15:25:52.944+08:00 level=INFO source=llama_server.go:1315 msg="llama-server started in 21.86 seconds"
time=2026-10-04T15:25:52.944+08:00 level=DEBUG source=sched.go:751 msg="finished setting up" runner.name=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M runner.size="1.7 GiB" runner.vram="0 B" runner.parallel=1 runner.pid=3614723 runner.model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb runner.num_ctx=4096
[GIN] 2026/10/04 - 15:25:52 | 200 | 22.278350667s | 127.0.0.1 | POST "/api/generate"
time=2026-10-04T15:25:52.956+08:00 level=DEBUG source=sched.go:759 msg="context for request finished"
time=2026-10-04T15:25:52.956+08:00 level=DEBUG source=sched.go:400 msg="runner with non-zero duration has gone idle, adding timer" runner.name=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M runner.size="1.7 GiB" runner.vram="0 B" runner.parallel=1 runner.pid=3614723 runner.model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb runner.num_ctx=4096 duration=5m0s
time=2026-10-04T15:25:52.956+08:00 level=DEBUG source=sched.go:418 msg="after processing request finished event" runner.name=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M runner.size="1.7 GiB" runner.vram="0 B" runner.parallel=1 runner.pid=3614723 runner.model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb runner.num_ctx=4096 refCount=0
time=2026-10-04T15:25:59.421+08:00 level=DEBUG source=sched.go:1394 msg="evaluating already loaded" model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb
time=2026-10-04T15:25:59.464+08:00 level=DEBUG source=llama_server.go:1835 msg="llama-server chat request" messages=1 tools=0
srv operator(): chat format: peg-native
slot get_availabl: id 0 | task -1 | selected slot by LRU, t_last = -1
srv get_availabl: updating prompt cache
srv load: - looking for better prompt, base f_keep = -1.000, sim = 0.000
srv update: - cache state: 0 prompts, 0.000 MiB (limits: 8192.000 MiB, 4096 tokens, 8589934592 est)
srv get_availabl: prompt cache update took 0.33 ms
slot launch_slot_: id 0 | task -1 | sampler chain: logits -> penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> ?min-p -> ?xtc -> temp-ext -> dist 
slot launch_slot_: id 0 | task -1 | sampler params: 
        repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
        dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
        top_k = 40, top_p = 0.900, min_p = 0.000, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000, adaptive_target = -1.000, adaptive_decay = 0.900
slot launch_slot_: id 0 | task 0 | processing task, is_child = 0
slot operator(): id 0 | task 0 | new prompt, n_ctx_slot = 4096, n_keep = 4, task.n_tokens = 12
slot operator(): id 0 | task 0 | cached n_tokens = 0, memory_seq_rm [0, end)
srv stream_sessi: conv_id= (empty=1)
slot print_timing: id 0 | task 0 | prompt processing, n_tokens = 1, progress = 0.08, t = 8.82 s / 0.11 tokens per second
slot operator(): id 0 | task 0 | cached n_tokens = 1, memory_seq_rm [1, end)
slot init_sampler: id 0 | task 0 | init sampler, took 4.68 ms, tokens: text = 12, total = 12
[GIN] 2026/10/04 - 15:26:17 | 200 | 18.389665931s | 127.0.0.1 | POST "/api/chat"
time=2026-10-04T15:26:17.433+08:00 level=DEBUG source=sched.go:495 msg="context for request finished" runner.name=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M runner.size="1.7 GiB" runner.vram="0 B" runner.parallel=1 runner.pid=3614723 runner.model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb runner.num_ctx=4096
time=2026-10-04T15:26:17.444+08:00 level=DEBUG source=sched.go:400 msg="runner with non-zero duration has gone idle, adding timer" runner.name=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M runner.size="1.7 GiB" runner.vram="0 B" runner.parallel=1 runner.pid=3614723 runner.model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb runner.num_ctx=4096 duration=5m0s
time=2026-10-04T15:26:17.445+08:00 level=DEBUG source=sched.go:418 msg="after processing request finished event" runner.name=hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M runner.size="1.7 GiB" runner.vram="0 B" runner.parallel=1 runner.pid=3614723 runner.model=/home/user/.ollama/models/blobs/sha256-86e63a5218a10e095f5ed1dcf2ef1c1a9dd9af6416f4d8e9288c7562f9edd2cb runner.num_ctx=4096 refCount=0
srv stop: cancel task, id_task = 0
slot release: id 0 | task 0 | stop processing: n_tokens = 25, truncated = 0
srv update_slots: all slots are idle
[GIN] 2026/10/04 - 15:26:24 | 200 | 2.911481ms | 127.0.0.1 | HEAD "/"
[GIN] 2026/10/04 - 15:26:24 | 200 | 2.257318ms | 127.0.0.1 | GET "/api/ps"

```

and also this:

```auto
 user   ~    ollama ls
NAME ID SIZE MODIFIED     
hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M 6263d7d0bd60 1.6 GB 2 weeks ago     
gemma4:e2b 7fbdbf8f5e45 7.2 GB 3 weeks ago     
gemma4:e4b c6eb396dbd59 9.6 GB 3 weeks ago     
minimax-m2.7:cloud 06daa293c105 - 4 months ago    
minimax-m2.5:cloud c0d5751c800f - 4 months ago    
 user   ~    ollama run hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M
>>> hello
Thinking...
Hmm, the user said "hello". This is a simple^C>>> /bye
 user   ~    ollama ps
NAME ID SIZE PROCESSOR CONTEXT UNTIL              
hf.co/mradermacher/MiniCPM5-2B-heretic-abliterated-i1-GGUF:Q4_K_M 6263d7d0bd60 1.8 GB 100% CPU 4096 4 minutes from now    
 user   ~    

```

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [October 5, 2026, 7:57am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/9 "2026-10-05T07:57:45Z")

</div>

> [@cylin577](#):
>
> ```auto
> 0.00.342.221 E ggml_cuda_init: failed to initialize CUDA: unknown error
> 
> ```

Hi,

According to the message above, please try to validate the CUDA functionality with the below sample first:

> **[cuda-samples/cpp/1\_Utilities/deviceQuery at master · NVIDIA/cuda-samples](https://github.com/NVIDIA/cuda-samples/tree/master/cpp/1_Utilities/deviceQuery)**
>
> master/cpp/1\_Utilities/deviceQuery

Thanks.

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 5, 2026, 9:38am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/10 "2026-10-05T09:38:57Z")

</div>

Hello, here is the result:

```auto
 user   deviceQuery   master ≡  ?14    ./deviceQuery 
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

cudaGetDeviceCount returned 999
-> unknown error
Result = FAIL
 user   deviceQuery   master ≡  ?14    sudo ./deviceQuery
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

cudaGetDeviceCount returned 999
-> unknown error
Result = FAIL
 user   deviceQuery   master ≡  ?14    

```

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [October 7, 2026, 7:20am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/11 "2026-10-07T07:20:24Z")

</div>

Hi,

It looks like your CUDA is not working.  
Could you share the way you set up the device?

What is the output of the command below?

```auto
$ nvidia-smi

```

Thanks.

---

<div class="post-metadata">

**Author:** ![alejandro.godinez](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/alejandro.godinez/32/540227_2.png) [@alejandro.godinez](https://forums.developer.nvidia.com/u/alejandro.godinez)\
**Post date:** [October 9, 2026, 9:43pm UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/12 "2026-10-09T21:43:23Z")

</div>

Hi cylin577,

Your `deviceQuery` results confirm that CUDA initialization is failing outside Ollama as well. Since it also fails with `sudo`, we should check the installation and driver logs before changing more Ollama settings.

To follow up on @AastaLLL question, could you provide the following?

1. How did you install JetPack 7.2.1—through the Jetson ISO, SDK Manager, or an upgrade of an existing installation? Was CUDA installed separately afterward? Please also share the current output of `nvidia-smi`, `cat /etc/nv_tegra_release`, and `uname -r`.

2. After rebooting, close LM Studio and stop Ollama if it starts automatically:

3. If it still fails, capture the kernel log immediately afterward:

If the kernel log does not explain the failure, this additional command will show which CUDA driver library the sample actually loads:

```auto
LD_DEBUG=libs,files ./deviceQuery > deviceQuery-loader.log 2>&1

```

Please attach `deviceQuery-loader.log` as well. This is more useful than relying on the CUDA version in `PATH` or the library cache.

These results should help narrow down the failure before deciding whether any packages need replacing.

Best regards,  
Alejandro Godínez  
Embedded Software Engineer, RidgeRun

Contact us: [support@ridgerun.com](mailto:support@ridgerun.com)  
Developers Wiki: [https://developer.ridgerun.com](https://developer.ridgerun.com)  
Website: [https://www.ridgerun.com](https://www.ridgerun.com)

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 12, 2026, 1:26pm UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/14 "2026-10-12T13:26:22Z")

</div>

I use the NVIDIA SDK Manager to flash my Jetson, for the first few month it worked perfectly, but just a few weeks ago this issue starts to happen

```auto
 user   ~    nvidia-smi
Mon Oct 12 21:25:05 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.78 Driver Version: 595.78 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 Orin (nvgpu) N/A | N/A N/A | N/A |
| N/A N/A N/A N/A / N/A | Not Supported | N/A N/A |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
 user   ~    

```

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 12, 2026, 1:29pm UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/15 "2026-10-12T13:29:43Z")

</div>

1. SDK Manager

```auto
 user   ~    nvidia-smi
Mon Oct 12 21:27:37 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 595.78 Driver Version: 595.78 CUDA Version: 13.2 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 Orin (nvgpu) N/A | N/A N/A | N/A |
| N/A N/A N/A N/A / N/A | Not Supported | N/A N/A |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
 user   ~    cat /etc/nv_tegra_release
# R39 (release), REVISION: 2.1, GCID: 46758480, BOARD: generic, EABI: aarch64, DATE: Fri Aug 7 05:54:22 AM UTC 2026
# KERNEL_VARIANT: oot
TARGET_USERSPACE_LIB_DIR=nvidia
TARGET_USERSPACE_LIB_DIR_PATH=usr/lib/aarch64-linux-gnu/nvidia
 user   ~    uname -r
6.8.12-1021-tegra
 user   ~    cd cuda-samples/cpp/1_Utilities/deviceQuery
 user   deviceQuery   master ≡  ?14    free -h
./deviceQuery
               total used free shared buff/cache available
Mem: 7.4Gi 6.6Gi 209Mi 77Mi 906Mi 823Mi
Swap: 31Gi 10Gi 21Gi
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

cudaGetDeviceCount returned 999
-> unknown error
Result = FAIL
 user   deviceQuery   master ≡  ?14    sudo journalctl -k -b --no-pager > kernel-current-boot.log
 user   deviceQuery   master ≡  ?15    ls -la
total 136
drwxrwxr-x 4 user user 4096 Oct 12 21:28 .
drwxrwxr-x 6 user user 4096 Jul 1 17:32 ..
drwxrwxr-x 3 user user 4096 Jul 1 17:32 CMakeFiles
-rw-rw-r-- 1 user user 13572 Jul 1 17:32 cmake_install.cmake
-rw-rw-r-- 1 user user 1356 Jul 1 17:32 CMakeLists.txt
-rwxrwxr-x 1 user user 72408 Jul 1 17:32 deviceQuery
-rw-rw-r-- 1 user user 15433 Jul 1 17:32 deviceQuery.cpp
-rw-rw-r-- 1 user user 47062 Oct 12 21:28 kernel-current-boot.log
-rw-rw-r-- 1 user user 8161 Jul 1 17:32 Makefile
-rw-rw-r-- 1 user user 1503 Jul 1 17:32 README.md
drwxrwxr-x 2 user user 4096 Jul 1 17:32 .vscode
 user   deviceQuery   master ≡  ?15    ls -lah
total 136K
drwxrwxr-x 4 user user 4.0K Oct 12 21:28 .
drwxrwxr-x 6 user user 4.0K Jul 1 17:32 ..
drwxrwxr-x 3 user user 4.0K Jul 1 17:32 CMakeFiles
-rw-rw-r-- 1 user user 14K Jul 1 17:32 cmake_install.cmake
-rw-rw-r-- 1 user user 1.4K Jul 1 17:32 CMakeLists.txt
-rwxrwxr-x 1 user user 71K Jul 1 17:32 deviceQuery
-rw-rw-r-- 1 user user 16K Jul 1 17:32 deviceQuery.cpp
-rw-rw-r-- 1 user user 46K Oct 12 21:28 kernel-current-boot.log
-rw-rw-r-- 1 user user 8.0K Jul 1 17:32 Makefile
-rw-rw-r-- 1 user user 1.5K Jul 1 17:32 README.md
drwxrwxr-x 2 user user 4.0K Jul 1 17:32 .vscode
 user   deviceQuery   master ≡  ?15    LD_DEBUG=libs,files ./deviceQuery > deviceQuery-loader.log 2>&1
 user   deviceQuery   master ≡  ?16    ls -lah
total 160K
drwxrwxr-x 4 user user 4.0K Oct 12 21:28 .
drwxrwxr-x 6 user user 4.0K Jul 1 17:32 ..
drwxrwxr-x 3 user user 4.0K Jul 1 17:32 CMakeFiles
-rw-rw-r-- 1 user user 14K Jul 1 17:32 cmake_install.cmake
-rw-rw-r-- 1 user user 1.4K Jul 1 17:32 CMakeLists.txt
-rwxrwxr-x 1 user user 71K Jul 1 17:32 deviceQuery
-rw-rw-r-- 1 user user 16K Jul 1 17:32 deviceQuery.cpp
-rw-rw-r-- 1 user user 22K Oct 12 21:28 deviceQuery-loader.log
-rw-rw-r-- 1 user user 46K Oct 12 21:28 kernel-current-boot.log
-rw-rw-r-- 1 user user 8.0K Jul 1 17:32 Makefile
-rw-rw-r-- 1 user user 1.5K Jul 1 17:32 README.md
drwxrwxr-x 2 user user 4.0K Jul 1 17:32 .vscode
 user   deviceQuery   master ≡  ?16    

```

[kernel-current-boot.log](https://forums.developer.nvidia.com/secure-uploads/nvidia/original/4X/b/d/3/bd3bd3683dcbb47fcc7289c2cb0c72eabab914de.log) (46.0 KB)  
[deviceQuery-loader.log](https://forums.developer.nvidia.com/secure-uploads/nvidia/original/4X/4/d/1/4d1b979cda3356b9cb9f8ca7e64fda1a254dc5db.log) (21.7 KB)

---

<div class="post-metadata">

**Author:** ![alejandro.godinez](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/alejandro.godinez/32/540227_2.png) [@alejandro.godinez](https://forums.developer.nvidia.com/u/alejandro.godinez)\
**Post date:** [October 13, 2026, 8:37pm UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/16 "2026-10-13T20:37:43Z")

</div>

Hi cylin577,

Thanks for collecting the logs. They show a GPU error that is useful to investigate:

At 21:14 the `nvgpu` driver reported SM exceptions, an `invalid pte` MMU fault, and FECS errors. Your `deviceQuery` test was run later, so I cannot yet tell whether that earlier event left the GPU in a bad state or whether CUDA fails from a fresh boot.

The loader log also confirms that `deviceQuery` is using the Jetson `libcuda.so.1` under `/opt/nvidia/l4t-gpu-libs/nvgpu/`. It then searches for `libnvcucompat.so` without finding it. Could you try these steps?

1. Fully shut down and power the Jetson back on. Before opening LM Studio or running Ollama, run `./deviceQuery` again and tell us whether it passes.

2. Check whether the compatibility library exists:

3. If the file exists under the `nvidia` directory but `deviceQuery` still fails, try this temporary library-path test from the `deviceQuery` directory:

Please share those results and, if the test still fails after the fresh boot, the new kernel messages from that boot. This will tell us whether to focus on the GPU fault or on the library setup before changing packages.

One more detail that may help: is this the NVIDIA P3768 developer kit carrier or a custom carrier board? If it is custom, please share its model, the BSP/board configuration used when flashing, and whether the carrier, power supply, or BSP changed shortly before the issue began. The kernel log contains GPU faults, so these details would help us check the board integration without assuming it caused the CUDA failure.

Best regards,  
Alejandro Godínez  
Embedded Software Engineer, RidgeRun

Contact us: [support@ridgerun.com](mailto:support@ridgerun.com)  
Developers Wiki: [https://developer.ridgerun.com](https://developer.ridgerun.com)  
Website: [https://www.ridgerun.com](https://www.ridgerun.com)

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [October 14, 2026, 7:59am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/17 "2026-10-14T07:59:45Z")

</div>

Hi,

Could you try to reboot the system and run the deviceQuery again?  
If the issue still occurs, please share the corresponding logs with us.

Thanks.

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 18, 2026, 2:09am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/18 "2026-10-18T02:09:44Z")

</div>

Here are the ls outputs (The file doesnt exist so i didnt run deviceQuery)

```auto
 user   ~    ls -l /usr/lib/aarch64-linux-gnu/libnvcucompat.so /usr/lib/aarch64-linux-gnu/nvidia/libnvcucompat.so
ldconfig -p | grep libnvcucompat
ls: cannot access '/usr/lib/aarch64-linux-gnu/libnvcucompat.so': No such file or directory
ls: cannot access '/usr/lib/aarch64-linux-gnu/nvidia/libnvcucompat.so': No such file or directory
 user   ~    ls -l /usr/lib/aarch64-linux-gnu/nvidia/
total 523180
-rwxr-xr-x 1 root root 10414160 Feb 12 2026 libcudadebugger.so.1
-rw-r--r-- 1 root root 1269208 Aug 7 13:41 libGLX_nvidia.so.0
-rw-r--r-- 1 root root 567768 Aug 7 13:41 libgnarl-25.so
lrwxrwxrwx 1 root root 14 Aug 7 13:41 libgnarl.so -> libgnarl-25.so
-rw-r--r-- 1 root root 6681216 Aug 7 13:41 libgnat-25.so
lrwxrwxrwx 1 root root 13 Aug 7 13:41 libgnat.so -> libgnat-25.so
lrwxrwxrwx 1 root root 29 Aug 7 13:41 libgstnvcustomhelper.so -> libgstnvcustomhelper.so.1.0.0
-rw-r--r-- 1 root root 67232 Aug 7 13:41 libgstnvcustomhelper.so.1.0.0
lrwxrwxrwx 1 root root 26 Aug 7 13:41 libgstnvdsseimeta.so -> libgstnvdsseimeta.so.1.0.0
-rw-r--r-- 1 root root 67456 Aug 7 13:41 libgstnvdsseimeta.so.1.0.0
-rw-r--r-- 1 root root 67808 Aug 7 13:41 libjetsonpower.so
-rw-r--r-- 1 root root 1642904 Aug 7 13:41 libnvargus.so
-rw-r--r-- 1 root root 2429400 Aug 7 13:41 libnvargus_socketclient.so
-rw-r--r-- 1 root root 2435656 Aug 7 13:41 libnvargus_socketserver.so
-rw-r--r-- 1 root root 789728 Aug 7 13:41 libnvbuf_deinterlace_gpu.so
lrwxrwxrwx 1 root root 29 Aug 7 13:41 libnvbuf_deinterlace.so -> libnvbuf_deinterlace.so.1.0.0
-rw-r--r-- 1 root root 67440 Aug 7 13:41 libnvbuf_deinterlace.so.1.0.0
-rw-r--r-- 1 root root 69320 Aug 7 13:41 libnvbuf_deinterlace_vic.so
-rw-r--r-- 1 root root 133576 Aug 7 13:41 libnvbuf_fdmap.so.1.0.0
lrwxrwxrwx 1 root root 30 Aug 7 13:41 libnvbufsurface_nvsci.so -> libnvbufsurface_nvsci.so.1.0.0
-rw-r--r-- 1 root root 73432 Aug 7 13:41 libnvbufsurface_nvsci.so.1.0.0
lrwxrwxrwx 1 root root 24 Aug 7 13:41 libnvbufsurface.so -> libnvbufsurface.so.1.0.0
-rw-r--r-- 1 root root 866920 Aug 7 13:41 libnvbufsurface.so.1.0.0
lrwxrwxrwx 1 root root 30 Aug 7 13:41 libnvbufsurftransform.so -> libnvbufsurftransform.so.1.0.0
-rw-r--r-- 1 root root 39993312 Aug 7 13:41 libnvbufsurftransform.so.1.0.0
-rw-r--r-- 1 root root 1183032 Aug 7 13:41 libnvcamerahal.so
-rw-r--r-- 1 root root 1249400 Aug 7 13:41 libnvcameratools.so
-rw-r--r-- 1 root root 330704 Aug 7 13:41 libnvcamerautils.so
-rw-r--r-- 1 root root 67376 Aug 7 13:41 libnvcam_fsync.so
-rw-r--r-- 1 root root 67760 Aug 7 13:41 libnvcam_imageencoder.so
-rw-r--r-- 1 root root 68864 Aug 7 13:41 libnvcamlog.so
-rw-r--r-- 1 root root 199232 Aug 7 13:41 libnvcamv4l2.so
-rw-r--r-- 1 root root 398584 Aug 7 13:41 libnvcapture.so
-rw-r--r-- 1 root root 133376 Aug 7 13:41 libnvcolorutil.so
-rw-r--r-- 1 root root 67696 Aug 7 13:41 libnvdc.so
-rw-r--r-- 1 root root 133320 Aug 7 13:41 libnvddk_2d_v2.so
-rw-r--r-- 1 root root 76616 Aug 7 13:41 libnvddk_vic.so
-rw-r--r-- 1 root root 67736 Aug 7 13:41 libnvdecode2eglimage.so
-rw-r--r-- 1 root root 3542880 Aug 7 13:41 libnvdi.so
-rw-r--r-- 1 root root 8159168 Nov 20 2024 libnvdla_compiler.so
-rw-r--r-- 1 root root 7347616 Aug 7 13:41 libnvdla_runtime.so
lrwxrwxrwx 1 root root 26 Aug 7 13:41 libnvdsbufferpool.so -> libnvdsbufferpool.so.1.0.0
-rw-r--r-- 1 root root 67696 Aug 7 13:41 libnvdsbufferpool.so.1.0.0
-rw-r--r-- 1 root root 67552 Aug 7 13:41 libnveglstreamproducer.so
-rw-r--r-- 1 root root 67536 Aug 7 13:41 libnveventlib.so
-rw-r--r-- 1 root root 67432 Aug 7 13:41 libnvexif.so
-rw-r--r-- 1 root root 789704 Aug 7 13:41 libnvfnet.so
-rw-r--r-- 1 root root 330248 Aug 7 13:41 libnvfnetstoredefog.so
-rw-r--r-- 1 root root 264768 Aug 7 13:41 libnvfnetstorehdfx.so
-rw-r--r-- 1 root root 199136 Aug 7 13:41 libnvfusacapext.so
-rw-r--r-- 1 root root 199192 Aug 7 13:41 libnvfusacapinterface.so
-rw-r--r-- 1 root root 396016 Aug 7 13:41 libnvfusacap.so
-rw-r--r-- 1 root root 24275544 Aug 7 13:41 libnvidia-eglcore.so.595.78
lrwxrwxrwx 1 root root 31 Aug 7 13:41 libnvidia-egl-wayland.so.1 -> libnvidia-egl-wayland.so.1.1.11
-rw-r--r-- 1 root root 73176 Aug 7 13:41 libnvidia-egl-wayland.so.1.1.11
-rw-r--r-- 1 root root 268480 Aug 7 13:41 libnvidia-encode.so
lrwxrwxrwx 1 root root 19 Aug 7 13:41 libnvidia-encode.so.1 -> libnvidia-encode.so
-rw-r--r-- 1 root root 25966824 Aug 7 13:41 libnvidia-glcore.so.595.78
-rw-r--r-- 1 root root 644840 Aug 7 13:41 libnvidia-glsi.so.595.78
-rw-r--r-- 1 root root 9945824 Aug 7 13:41 libnvidia-glvkspirv.so.595.78
-rw-r--r-- 1 root root 89554616 Aug 7 13:41 libnvidia-gpucomp.so.595.78
lrwxrwxrwx 1 root root 23 Aug 7 13:41 libnvidia-kms.so -> libnvidia-kms.so.595.78
-rw-r--r-- 1 root root 88640 Aug 7 13:41 libnvidia-kms.so.595.78
-rw-r--r-- 1 root root 2559944 Aug 7 13:41 libnvidia-ml.so.1
lrwxrwxrwx 1 root root 26 Aug 7 13:41 libnvidia-nvvm70.so.4 -> libnvidia-nvvm70.so.595.78
-rw-r--r-- 1 root root 21494664 Aug 7 13:41 libnvidia-nvvm70.so.595.78
lrwxrwxrwx 1 root root 24 Aug 7 13:41 libnvidia-nvvm.so.4 -> libnvidia-nvvm.so.595.78
-rw-r--r-- 1 root root 22057088 Aug 7 13:41 libnvidia-nvvm.so.595.78
-rw-r--r-- 1 root root 67664 Aug 7 13:41 libnvidia-opticalflow.so
lrwxrwxrwx 1 root root 24 Aug 7 13:41 libnvidia-opticalflow.so.1 -> libnvidia-opticalflow.so
lrwxrwxrwx 1 root root 34 Aug 7 13:41 libnvidia-ptxjitcompiler.so.1 -> libnvidia-ptxjitcompiler.so.595.78
-rw-r--r-- 1 root root 32367296 Aug 7 13:41 libnvidia-ptxjitcompiler.so.595.78
-rw-r--r-- 1 root root 377568 Aug 7 13:41 libnvidia-rmapi-tegra.so.595.78
-rw-r--r-- 1 root root 29633000 Aug 7 13:41 libnvidia-rtcore.so.595.78
-rw-r--r-- 1 root root 93664856 Aug 7 13:41 libnvidia-tileiras.so.595.78
-rw-r--r-- 1 root root 10312 Aug 7 13:41 libnvidia-tls.so.595.78
lrwxrwxrwx 1 root root 23 Aug 7 13:41 libnvid_mapper.so -> libnvid_mapper.so.1.0.0
-rw-r--r-- 1 root root 724200 Aug 7 13:41 libnvid_mapper.so.1.0.0
-rw-r--r-- 1 root root 69552 Aug 7 13:41 libnvimp.so
-rw-r--r-- 1 root root 1511104 Aug 7 13:41 libnvisppg.so
-rw-r--r-- 1 root root 788984 Aug 7 13:41 libnvisp.so
-rw-r--r-- 1 root root 724088 Aug 7 13:41 libnvisp_utils.so
-rw-r--r-- 1 root root 133224 Aug 7 13:41 libnvmedia_2d.so
-rw-r--r-- 1 root root 264544 Aug 7 13:41 libnvmedia2d.so
-rw-r--r-- 1 root root 70000 Aug 7 13:41 libnvmedia_dla.so
-rw-r--r-- 1 root root 67480 Aug 7 13:41 libnvmedia_eglstream.so
-rw-r--r-- 1 root root 67800 Aug 7 13:41 libnvmedia_ide_parser.so
-rw-r--r-- 1 root root 67872 Aug 7 13:41 libnvmedia_ide_sci.so
-rw-r--r-- 1 root root 133448 Aug 7 13:41 libnvmedia_iep_sci.so
-rw-r--r-- 1 root root 67872 Aug 7 13:41 libnvmedia_ijpd_sci.so
-rw-r--r-- 1 root root 67864 Aug 7 13:41 libnvmedia_ijpe_sci.so
-rw-r--r-- 1 root root 67920 Aug 7 13:41 libnvmedia_iofa_sci.so
-rw-r--r-- 1 root root 133296 Aug 7 13:41 libnvmedia_isp_ext.so
-rw-r--r-- 1 root root 133336 Aug 7 13:41 libnvmedialdc.so
-rw-r--r-- 1 root root 396664 Aug 7 13:41 libnvmedia.so
-rw-r--r-- 1 root root 70520 Aug 7 13:41 libnvmedia_tensor.so
-rw-r--r-- 1 root root 134240 Aug 7 13:41 libnvmm_contentpipe.so
-rw-r--r-- 1 root root 1117472 Aug 7 13:41 libnvmm_jpeg.so
-rw-r--r-- 1 root root 67880 Aug 7 13:41 libnvmmlite_image.so
-rw-r--r-- 1 root root 67496 Aug 7 13:41 libnvmmlite.so
-rw-r--r-- 1 root root 67520 Aug 7 13:41 libnvmmlite_utils.so
-rw-r--r-- 1 root root 266240 Aug 7 13:41 libnvmmlite_video.so
-rw-r--r-- 1 root root 199040 Aug 7 13:41 libnvmm_parser.so
-rw-r--r-- 1 root root 67536 Aug 7 13:41 libnvmm.so
-rw-r--r-- 1 root root 133880 Aug 7 13:41 libnvmm_utils.so
-rw-r--r-- 1 root root 5717288 Aug 7 13:41 libnvodm_imager.so
-rw-r--r-- 1 root root 460872 Aug 7 13:41 libnvoggopus.so
-rw-r--r-- 1 root root 67528 Aug 7 13:41 libnvomxilclient.so
-rw-r--r-- 1 root root 468920 Aug 7 13:41 libnvomx.so
-rw-r--r-- 1 root root 11342544 Aug 7 13:41 libnvosd.so
-rw-r--r-- 1 root root 133904 Aug 7 13:41 libnvos.so
-rw-r--r-- 1 root root 264448 Aug 7 13:41 libnvparser.so
-rw-r--r-- 1 root root 67584 Aug 7 13:41 libnvplayfair.so
-rw-r--r-- 1 root root 160272 Aug 7 13:41 libnvpva_algorithms.so
-rw-r--r-- 1 root root 67528 Aug 7 13:41 libnvpva.so
-rw-r--r-- 1 root root 67848 Aug 7 13:41 libnvrm_chip.so
-rw-r--r-- 1 root root 461344 Aug 7 13:41 libnvrm_gpu.so
-rw-r--r-- 1 root root 133296 Aug 7 13:41 libnvrm_host1x.so
-rw-r--r-- 1 root root 68280 Aug 7 13:41 libnvrm_mem.so
-rw-r--r-- 1 root root 67704 Aug 7 13:41 libnvrm_stream.so
-rw-r--r-- 1 root root 207840 Aug 7 13:41 libnvrm_surface.so
-rw-r--r-- 1 root root 67784 Aug 7 13:41 libnvrm_sync.so
-rw-r--r-- 1 root root 12206632 Aug 7 13:41 libnvscf.so
lrwxrwxrwx 1 root root 16 Aug 7 13:41 libnvscibuf.so -> libnvscibuf.so.1
-rw-r--r-- 1 root root 452496 Aug 7 13:41 libnvscibuf.so.1
lrwxrwxrwx 1 root root 19 Aug 7 13:41 libnvscicommon.so -> libnvscicommon.so.1
-rw-r--r-- 1 root root 22424 Aug 7 13:41 libnvscicommon.so.1
-rw-r--r-- 1 root root 67504 Aug 7 13:41 libnvscievent.so
-rw-r--r-- 1 root root 133392 Aug 7 13:41 libnvsciipc.so
lrwxrwxrwx 1 root root 19 Aug 7 13:41 libnvscistream.so -> libnvscistream.so.1
-rw-r--r-- 1 root root 1730960 Aug 7 13:41 libnvscistream.so.1
lrwxrwxrwx 1 root root 17 Aug 7 13:41 libnvscisync.so -> libnvscisync.so.1
-rw-r--r-- 1 root root 265224 Aug 7 13:41 libnvscisync.so.1
-rw-r--r-- 1 root root 986576 Aug 7 13:41 libnvsipl_control.so
-rw-r--r-- 1 root root 133024 Aug 7 13:41 libnvsipl_devblk_cdi.so
-rw-r--r-- 1 root root 67424 Aug 7 13:41 libnvsipl_devblk_crypto.so
-rw-r--r-- 1 root root 732480 Aug 7 13:41 libnvsipl_devblk_ddi.so
-rw-r--r-- 1 root root 929488 Aug 7 13:41 libnvsipl_devblk.so
-rw-r--r-- 1 root root 1126864 Aug 7 13:41 libnvsipl_pipeline.so
-rw-r--r-- 1 root root 1322496 Aug 7 13:41 libnvsipl_query.so
-rw-r--r-- 1 root root 1126128 Aug 7 13:41 libnvsipl.so
-rw-r--r-- 1 root root 67792 Aug 7 13:41 libnvsocsys.so
-rw-r--r-- 1 root root 67440 Aug 7 13:41 libnvtegrahv.so
-rw-r--r-- 1 root root 67288 Aug 7 13:41 libnvtracebuf.so
-rw-r--r-- 1 root root 76392 Aug 7 13:41 libnvtvmr_2d.so
-rw-r--r-- 1 root root 947456 Aug 7 13:41 libnvtvmr.so
-rw-r--r-- 1 root root 1092496 Aug 7 13:41 libnvv4l2.so
-rw-r--r-- 1 root root 199200 Aug 7 13:41 libnvv4lconvert.so
-rw-r--r-- 1 root root 264488 Aug 7 13:41 libnvvic.so
-rw-r--r-- 1 root root 1445760 Aug 7 13:41 libnvvideoencode_ppe.so
-rw-r--r-- 1 root root 1577728 Aug 7 13:41 libnvvideo.so
-rw-r--r-- 1 root root 789992 Aug 7 13:41 libtegrawfd.so
-rw-r--r-- 1 root root 987728 Aug 7 13:41 libv4l2_nvargus.so
lrwxrwxrwx 1 root root 12 Aug 7 13:41 libv4l2.so.0 -> libnvv4l2.so
lrwxrwxrwx 1 root root 18 Aug 7 13:41 libv4lconvert.so.0 -> libnvv4lconvert.so
-rw-r--r-- 1 root root 10408832 Aug 7 13:41 libVkLayer_json_gen.so
-rw-r--r-- 1 root root 20160152 Aug 7 13:41 libVkSCLayer_khronos_validation.so
lrwxrwxrwx 1 root root 16 Aug 7 13:41 libvulkansc.so -> libvulkansc.so.1
lrwxrwxrwx 1 root root 21 Aug 7 13:41 libvulkansc.so.1 -> libvulkansc.so.1.0.10
-rw-r--r-- 1 root root 1578672 Aug 7 13:41 libvulkansc.so.1.0.10
lrwxrwxrwx 1 root root 20 Aug 7 13:41 libvulkan.so.1 -> libvulkan.so.1.4.321
-rw-r--r-- 1 root root 727544 Aug 7 13:41 libvulkan.so.1.4.321
-rw-r--r-- 1 root root 140 Aug 7 13:41 nvidia_icd.json
 user   ~    

```

And yes, this is the developer carrier board, i didnt touch it for months so i dont think thats the issue, I do have the kernel cmdline changed though:

```auto
APPEND ${cbootargs} root=PARTUUID=0cbdf715-c8bc-4b4b-81eb-9001da103a71 rw rootwait rootfstype=ext4 mminit_loglevel=4 console=ttyTCU0,115200 firmware_class.path=/etc/firmware fbcon=map:0 video=efifb:off console=tty0 efi_pstore.pstore_disable=1 pstore.backend=ramoops efi=runtime pci=pcie_bus_perf nvme.use_threaded_interrupts=1 swiotlb=2048 cma=4G

```

---

<div class="post-metadata">

**Author:** ![AastaLLL](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/aastalll/32/14043_2.png) [@AastaLLL](https://forums.developer.nvidia.com/u/AastaLLL)\
**Post date:** [October 19, 2026, 8:33am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/19 "2026-10-19T08:33:00Z")

</div>

Hi,

CUDA runtime library is located under `/usr/local/cuda-13.2/`.  
From JetPack 7.2, Orin uses the SBSA driver so there is not compat library.

Instead, you should see the SBSA version as follows:

```auto
$ ll /usr/local/cuda-13.2/
total 128
drwxr-xr-x 12 root root 4096 Sep 29 10:09 ./
drwxr-xr-x 11 root root 4096 Sep 29 10:11 ../
-rw-r--r-- 1 root root 160 May 8 20:43 DOCS
-rw-r--r-- 1 root root 68070 May 8 20:43 EULA.txt
-rw-r--r-- 1 root root 524 May 8 20:43 README
drwxr-xr-x 3 root root 4096 Sep 29 10:09 bin/
drwxr-xr-x 4 root root 4096 Sep 29 10:09 compute-sanitizer/
drwxr-xr-x 3 root root 4096 Sep 29 10:09 doc/
drwxr-xr-x 4 root root 4096 Sep 29 10:09 extras/
drwxr-xr-x 4 root root 4096 Sep 29 10:09 gds/
lrwxrwxrwx 1 root root 26 May 8 20:42 include -> targets/sbsa-linux/include/
lrwxrwxrwx 1 root root 22 May 8 21:17 lib64 -> targets/sbsa-linux/lib/
drwxr-xr-x 4 root root 4096 Sep 29 10:09 nvml/
drwxr-xr-x 6 root root 4096 Sep 29 10:09 nvvm/
drwxr-xr-x 3 root root 4096 Sep 29 10:09 share/
drwxr-xr-x 3 root root 4096 Sep 29 10:09 targets/
drwxr-xr-x 2 root root 4096 Sep 29 10:09 tools/
-rw-r--r-- 1 root root 3263 Jun 23 14:37 version.json

```

Thanks.

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 19, 2026, 12:56pm UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/20 "2026-10-19T12:56:19Z")

</div>

Hello, this is the output of the `ll` command:

```auto
 user   ~    ll /usr/local/cuda-13.2/
total 128
drwxr-xr-x 12 root root 4096 Sep 5 11:08 ./
drwxr-xr-x 14 root root 4096 Sep 21 09:52 ../
drwxr-xr-x 3 root root 4096 Sep 5 11:08 bin/
drwxr-xr-x 4 root root 4096 Sep 5 11:08 compute-sanitizer/
drwxr-xr-x 3 root root 4096 Sep 5 11:05 doc/
-rw-r--r-- 1 root root 160 May 9 04:43 DOCS
-rw-r--r-- 1 root root 68070 May 9 04:43 EULA.txt
drwxr-xr-x 4 root root 4096 Sep 5 11:08 extras/
drwxr-xr-x 4 root root 4096 Sep 5 11:05 gds/
lrwxrwxrwx 1 root root 26 Mar 18 2026 include -> targets/sbsa-linux/include/
lrwxrwxrwx 1 root root 22 Mar 18 2026 lib64 -> targets/sbsa-linux/lib/
drwxr-xr-x 4 root root 4096 Sep 5 11:08 nvml/
drwxr-xr-x 6 root root 4096 Sep 5 11:05 nvvm/
-rw-r--r-- 1 root root 524 May 9 04:43 README
drwxr-xr-x 3 root root 4096 Sep 5 11:08 share/
drwxr-xr-x 3 root root 4096 Jun 6 23:18 targets/
drwxr-xr-x 2 root root 4096 Sep 5 11:08 tools/
-rw-r--r-- 1 root root 3263 Jun 23 22:37 version.json
 user   ~    

```

---

<div class="post-metadata">

**Author:** ![Andrey1984](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/andrey1984/32/14130_2.png) [@Andrey1984](https://forums.developer.nvidia.com/u/Andrey1984)\
**Post date:** [October 19, 2026, 2:19pm UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/21 "2026-10-19T14:19:44Z")

</div>

**Here is a potential AI-suggested workaround based on the SBSA path changes in JetPack 7.x:**  
this issue on JetPack 7.2 (running CUDA 13.2 on Jetson Orin Nano) stems from the transition to the **SBSA (Server Base System Architecture) driver stack**.

Because the directory layout changed and legacy compatibility libraries were removed, applications, prebuilt binaries, and runtime wrappers (such as Ollama, LM Studio, or standalone CUDA samples like `deviceQuery`) fail with `cudaGetDeviceCount returned 999 -> unknown error` because they look in old hardcoded paths (`/usr/local/cuda/lib64`) instead of the new SBSA target tree (`/usr/local/cuda-13.2/targets/sbsa-linux/`).

Here is how to resolve the runtime detection failure:

### 1. Establish Proper Symlinks

Ensure that `/usr/local/cuda` points cleanly to the CUDA 13.2 installation, and verify that the `lib64` and `include` paths resolve to the SBSA target architecture:

```auto
sudo rm -f /usr/local/cuda
sudo ln -s /usr/local/cuda-13.2 /usr/local/cuda

```

### 2. Configure Global Environment Variables

Add the CUDA binary and SBSA library paths to your shell profile (`~/.bashrc` or `/etc/profile`):

```auto
export CUDA_HOME=/usr/local/cuda
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/targets/sbsa-linux/lib:$LD_LIBRARY_PATH

```

Run `source ~/.bashrc` to apply the changes in your current shell.

### 3. Fix Systemd Services (for Ollama, etc.)

If background services like Ollama are failing to detect the GPU, systemd drops standard user environment variables. Edit the service configuration explicitly:

```auto
sudo systemctl edit ollama.service

```

Add the following override block:

```auto
[Service]
Environment="PATH=/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
Environment="LD_LIBRARY_PATH=/usr/local/cuda/targets/sbsa-linux/lib"
Environment="OLLAMA_DEBUG=2"
Environment="OLLAMA_IGPU_ENABLE=1"

```

Save and restart the daemon:

```auto
sudo systemctl daemon-reload
sudo systemctl restart ollama.service

```

### 4. Re-validate with `deviceQuery`

Navigate to your compiled `deviceQuery` sample directory and run it with the explicit library path loaded:

```auto
export LD_LIBRARY_PATH=/usr/local/cuda/targets/sbsa-linux/lib:$LD_LIBRARY_PATH
./deviceQuery

```

It should now return `Result = PASS` with correct device identification for the Orin Nano (`sm_87`).

_Note: If you are using third-party containers or prebuilt binaries (like certain releases of Ollama or Python wheels), ensure they are built against CUDA 13.2 / `sm_87` compatibility, as older prebuilt packages lack native kernels for the JP7 SBSA layout._

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 20, 2026, 9:54am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/22 "2026-10-20T09:54:15Z")

</div>

Which should i set as CUDA\_HOME???

```auto
 user   local    ls -la
total 56
drwxr-xr-x 14 root root 4096 Sep 21 09:52 .
drwxr-xr-x 13 root root 4096 Sep 21 13:31 ..
drwxr-xr-x 4 root root 4096 Oct 17 14:11 bin
lrwxrwxrwx 1 root root 22 Sep 21 09:52 cuda -> /etc/alternatives/cuda
lrwxrwxrwx 1 root root 25 Jun 6 23:19 cuda-13 -> /etc/alternatives/cuda-13
drwxr-xr-x 12 root root 4096 Sep 5 11:08 cuda-13.2
drwxr-xr-x 4 root root 4096 Jun 11 17:44 etc
drwxr-xr-x 2 root root 4096 Apr 23 2024 games
drwxr-xr-x 10 root root 4096 Jul 2 05:24 go
drwxr-xr-x 8 root root 4096 Jul 14 17:37 include
drwxr-xr-x 2 root root 4096 Jun 7 08:19 jtop
drwxr-xr-x 7 root root 4096 Oct 17 14:02 lib
lrwxrwxrwx 1 root root 9 Apr 23 2024 man -> share/man
drwxr-xr-x 6 root root 4096 Jun 6 18:37 pipx
drwxr-xr-x 2 root root 4096 Sep 2 20:10 sbin
drwxr-xr-x 18 root root 4096 Oct 17 14:02 share
drwxr-xr-x 2 root root 4096 Apr 23 2024 src
 user   local    cd cuda-13.2/
 user   cuda-13.2    ls
bin compute-sanitizer doc DOCS EULA.txt extras gds include lib64 nvml nvvm README share targets tools version.json
 user   cuda-13.2    

```

---

<div class="post-metadata">

**Author:** ![Andrey1984](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/andrey1984/32/14130_2.png) [@Andrey1984](https://forums.developer.nvidia.com/u/Andrey1984)\
**Post date:** [October 20, 2026, 10:01am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/23 "2026-10-20T10:01:42Z")

</div>

> [@Andrey1984](#):
>
> Here is a potential AI-suggested workaround based on the SBSA path changes in JetPack 7.x

_(Disclaimer: The following step-by-step guidance was drafted with AI assistance based on JetPack 7.x SBSA changes)._

For **JetPack 7.2 (CUDA 13.2)** on Jetson Orin Nano, you should point your environment to the direct installation directory:

```auto
export CUDA_HOME=/usr/local/cuda-13.2

```

### Why & How to Set It Up

Because JetPack 7.x transitions Jetson Linux to the **SBSA (Server Base System Architecture)** directory structure, library targets are located under `targets/sbsa-linux/`.

To ensure system-wide compatibility across builds and runtimes (like Ollama or CUDA samples), add the following block to your `~/.bashrc` file:

```auto
# CUDA 13.2 / JetPack 7.2 Environment Setup
export CUDA_HOME=/usr/local/cuda-13.2
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/targets/sbsa-linux/lib:$LD_LIBRARY_PATH

```

After adding those lines, apply them to your active shell by running:

```auto
source ~/.bashrc

```

> **Note on Symlinks:** While `/usr/local/cuda` exists as a symlink (via `/etc/alternatives/cuda`), referencing `/usr/local/cuda-13.2` explicitly prevents issues if alternative package updates alter system symlinks later.

---

<div class="post-metadata">

**Author:** ![cylin577](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@cylin577](https://forums.developer.nvidia.com/u/cylin577)\
**Post date:** [October 20, 2026, 10:04am UTC](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847/25 "2026-10-20T10:04:09Z")

</div>

I’ve set it as /usr/local/cuda-13.2 but this is the deviceQuery result:

```auto
 user   ~    export CUDA_HOME=/usr/local/cuda-13
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/targets/sbsa-linux/lib:$LD_LIBRARY_PATH
cuda-13/ cuda-13.2/ 
 user   ~    export CUDA_HOME=/usr/local/cuda-13                 
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/targets/sbsa-linux/lib:$LD_LIBRARY_PATH
cuda-13/ cuda-13.2/ 
 user   ~    export CUDA_HOME=/usr/local/cuda-13.2               
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/targets/sbsa-linux/lib:$LD_LIBRARY_PATH
 user   ~    ~/cuda-samples/cpp/1_Utilities/deviceQuery/deviceQuery
/home/user/cuda-samples/cpp/1_Utilities/deviceQuery/deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

cudaGetDeviceCount returned 999
-> unknown error
Result = FAIL
 user   ~    

```

[Next page](https://forums.developer.nvidia.com/t/cuda-device-cant-be-detected-on-jetson-orin-nano-running-jp7/383847.md?page=2)