TensorRT-LLM for Jetson
TensorRT-LLM
is a high-performance LLM inference library with advanced quantization, attention kernels, and paged KV caching. Initial support for building TensorRT-LLM from source for JetPack 6.1 has been included in the
v0.12.0-jetson
branch of the
TensorRT-LLM repo
for Jetson AGX Orin.
We've provided pre-compiled TensorRT-LLM
wheels
and containers along with this guide for
TensorRT-LLM Deployment on Jetson Orin
What you need
-
One of the following Jetson devices:
Jetson AGX Orin *Support for other Orin devices is currently undergoing testing.
-
Running one of the following versions of JetPack :
JetPack 6.1 (L4T r36.4)
-
Sufficient storage space (preferably with NVMe SSD).
-
18.5GB
fortensorrt_llm
container image -
Space for models (
>10GB
)
-
-
Clone and setup
jetson-containers
:git clone https://github.com/dusty-nv/jetson-containers bash jetson-containers/install.sh
Building TensorRT-LLM Engine for Llama
You can find the steps for converting Llama to TensorRT-LLM under
examples/llama
in the repo, and also in the
documentation
. This script will automate the process for Llama-7B with INT4 quantization applied, and run some generation and performance checks on the model:
jetson-containers run \
-e HUGGINGFACE_TOKEN=YOUR_API_KEY \
-e FORCE_BUILD=on \
dustynv/tensorrt_llm:0.12-r36.4.0 \
/opt/TensorRT-LLM/llama.sh
There are many such conversion procedures outlined in the TensorRT-LLM examples for different model architectures.
OpenAI API Endpoint
TensorRT-LLM has programming APIs for Python and C++ available, but it also includes an example server endpoint for the OpenAI protocol that makes it easy to substitute for other local or cloud model backends.
This will start the TensorRT-LLM container with the server and model that you built above:
jetson-containers run \
dustynv/tensorrt_llm:0.12-r36.4.0 \
python3 /opt/TensorRT-LLM/examples/apps/openai_server.py \
/data/models/tensorrt_llm/Llama-2-7b-chat-hf-gptq
Then you can make chat completion requests against it from practically any language or from any connected device. This example shows a simple way of testing it initially from another terminal with curl:
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": <model_name>,
"prompt": "Where is New York?",
"max_tokens": 16,
"temperature": 0
}'
Or the code included with
openai_client.py
will handle these requests using the standard
openai-python
package that can be installed outside of the container or on another machine.
jetson-containers run \
--workdir /opt/TensorRT-LLM/examples/apps \
dustynv/tensorrt_llm:0.12-r36.4.0 \
python3 openai_client.py --prompt "Where is New York?" --api chat
The patches in the branch above for TensorRT-LLM 0.12 are a preview release for Jetson AGX Orin, and we'll continue with validating and testing the various settings in TensorRT-LLM. If you need any support, please post to the Jetson Developer Forums. .