General Question about jetson Xavier NX


1- In the jetson xavier nx , we have three accelerator (GPU/DLA1/DLA2) for using deep learning model, I want to know we can run three separate model on each accelerator simultaneously?
2- For running the AI models on DLA, we need to change the codes that we use for using GPU for jetson nano, GPU desktop? using of DLA for running deep model need difference codes? There two accelerator can be run any deep learning framework ? even TensorRT?
3- These two DLA only support INT8 ? or can support FP16? What about GPU? Can be support INT8 or only support FP16?
4- For running the inference codes how I can set the DLA1 to run model1 and DLA2 to run model2 and GPU to run model3?
5- The general structural of working with this device is similar with jetson nano or have large difference?

until you receive a more qualified answer, maybe the answers of this post I made bring you some light in some of your questions .

I’m quite sure GPU supports FP16 and INT8.
I’m quite sure DLAs only can run TensorTR models INT8 optimized
ISP,DLAs,7way VPU,x2 PCIe, make it quite different than jetson Nano even for camera support, I think you should look for AGX Xavier to get usable things.

Thanks a lot,
I want to know how i can assign one model to DLA1 and model 2 to DLA2?
In the multi-GPU Desktop, we can assign the specific model to GPU0 with the below command : or we also can set with os package.


For GPU=1:


we also can be use this:

os.environ[“CUDA_VISIBLE_DEVICES”]=“0” # for GPU0

For DLAs and GPU of Jetson xavier Nx How I can specific like the above?


1. Suppose yes.
But please noticed that DLA is hardware-based inference engine which limited the support operation range.
It’s recommended to check if your model can be fully deployed on the DLA first.

2. DLA can be enabled directly with TensorRT API:

nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(logger);
builder->setDLACore(0);        // or builder->setDLACore(1) for DLA1
nvinfer1::IRuntime* infer = nvinfer1::createInferRuntime(gLogger);
infer->setDLACore(0);          // or infer->setDLACore(1) for DLA1

3. FP16 and INT8.
4. Please check answer no.2.
5. Similar


Hi, LoveNvidia

Please noticed that DLA is a hardware process rather than GPU.
So the export command won’t assign the inference job to DLA but GPU.

Currently, DLA must be triggered from the TensorRT API.
Detail can be found in our document here:


Is there python bindings of TensorRT api to can assign DLA by a python?

Thanks a lots, @AastaLLL

Hi @AastaLLL
Is it possible to set DLACore to 0/1 with tensorflow-tensorrt integration api?
which version of tensorRT needed at least for DLA?

Hi @AastaLLL
For using Tensor cores of jetson xaiver nx, How can I use these cores? for DLA I have to use only TensorRT, For tencor cores also need onlt TensorRT? Is it possible to run the models with these codes directly like GPU?


You will need to use JetPack4.4 to get the XaveirNX support.
So please use TensorRT > 7.1.x to get the DLA support on NX.

TF-TRT doesn’t add DLA support.
For this, you can add the DLA support here and rebuild the TensorFlow package:


Tensor Core is part of GPU cores so you can access it directly with CUDA.



@AastaLLL, Thanks so much,
1- How I can to generate Tensorrt engine with trtexec file for TLT models? is it possible? How I can do it?
2- with trtexec is it possible to set converted engine to be run give DLA?


1. You will need to use Deepstream for the TLT format support:

2. YES. Please add --useDLACore=0 or --useDLACore=1 when executing.


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Thanks @AastaLLL,
Is also need to set allowGPUFallback=1? What this mean? allowGPUFallback, what’s happened?


If your model cannot be fully supported via DLA, this configure will replace the non-supported layer back to GPU implementation.


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NVVM Memory only related to jetson device?