Hello NVIDIA Developer Community,
I am testing local inference with DeepSeek-Coder-V2-Lite-Instruct 16B on an NVIDIA RTX 2050 laptop.
I have a recorded demonstration showing the model running locally and responding interactively.
I would appreciate technical feedback on:
Demo:
Thank you.
Hi @Waziri Dawoud — running a 16B coder model on an RTX 2050 laptop (4 GB VRAM, right?) is already impressive. Here’s a simple benchmarking setup that will give you numbers the community can actually compare against.
**Reproducible benchmark routine:**
1. Plug in the laptop and set NVIDIA Control Panel to “Prefer maximum performance.”
2. Close browsers and background apps.
3. Warm up with 3–5 throwaway generations.
4. Run each test config 5–10 times and report median + p95.
5. Fix input and output lengths, e.g., 512 input tokens, 256 output tokens.
**Metrics to report:**
- **TTFT** (time to first token)
- **Generation throughput** (tokens/sec)
- **End-to-end latency**
- **Peak VRAM** from `nvidia-smi`
**For fair comparisons:**
- Compare the same quantization level across frameworks.
- If you quantize, also report task accuracy/perplexity on a fixed benchmark like HumanEval.
- A fast model that gets answers wrong isn’t useful.
**Profiling tools I’d reach for:**
- **Nsight Systems** — best overall view of CPU/GPU timeline.
- **`nvidia-smi dmon -s mu`** — live utilization and memory.
- Framework built-ins: llama.cpp perplexity mode, Ollama’s token/sec logs, TensorRT-LLM benchmark scripts.
**Configs worth testing on the RTX 2050:**
- Q4_K_M quantization in llama.cpp — usually the sweet spot.
- INT4/FP8 via TensorRT-LLM if your GPU supports it.
- Partial CPU offloading as a fallback.
On 4 GB VRAM you’ll probably end up at 3-bit or with some layers on CPU. That’s totally fine — just document the quality impact honestly. That’s the most useful data for anyone else trying this.
Looking forward to seeing the numbers.
Hi Drew,
Thank you for the detailed feedback and benchmarking suggestions.
One important clarification: Bright AI did not use CPU inference offloading, and the model weights were not stored in system RAM. The DeepSeek-Coder-V2-Lite-Instruct 16B workload ran using the RTX 2050’s 4GB of dedicated GPU memory through Bright AI’s optimization system.
This is the key distinction between Bright AI and conventional hybrid inference approaches that depend on CPU offloading or system RAM when VRAM is insufficient.
We are preparing a new controlled demonstration with reproducible performance and memory measurements, including throughput, latency, dedicated VRAM usage, shared GPU memory usage, and system RAM usage. We will keep the proprietary implementation confidential while providing enough evidence for technical validation.
We welcome feedback from NVIDIA engineers, inference specialists, and relevant industry partners regarding the result and its potential for low-cost local AI deployment.
Bright AI
hello there
seem like no body care about this i could run 16 billion ai only thro 4 gb of the gpu ram , and still nobody care , i am about to give up if u think its worthy thing where we can aquire investmnet or make it somehting big reach out to me cuz i think nobody care about it ,
reach out if u think we can make it big cuz am alone and nobody care about it when i say it so reach out for partnership , am just tech guy i dont know how to promote or get investments or how to sell it on robots comapny where my ai can make thier roboto 10x faster
reach out if u think u can do soemthign with it ,