Model Overview
Description:
The NVIDIA Llama 3.1 8B Instruct FP8 model is the quantized version of the Meta's Llama 3.1 8B Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Llama 3.1 8B Instruct FP8 model is quantized with TensorRT Model Optimizer.
This model is ready for commercial and non-commercial use.
Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Meta-Llama-3.1-8B-Instruct) Model Card.
License/Terms of Use:
Model Architecture:
Architecture Type: Transformers Network Architecture: Llama3.1
Input:
Input Type(s): Text Input Format(s): String Input Parameters: Sequences Other Properties Related to Input: Context length up to 128K
Output:
Output Type(s): Text Output Format: String Output Parameters: Sequences Other Properties Related to Output: N/A
Software Integration:
Supported Runtime Engine(s):
- Tensor(RT)-LLM
- vLLM
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Blackwell
- NVIDIA Hopper
- NVIDIA Lovelace
Preferred Operating System(s):
- Linux
Model Version(s):
The model is quantized with nvidia-modelopt v0.27.0
Datasets:
- Calibration Dataset: cnn_dailymail
- Evaluation Dataset: MMLU
Inference:
Engine: Tensor(RT)-LLM or vLLM Test Hardware: H100
Post Training Quantization
This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP8 data type, ready for inference with TensorRT-LLM and vLLM. Only the weights and activations of the linear operators within transformers blocks are quantized. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. On H100, we achieved 1.3x speedup.
Usage
To deploy the quantized checkpoint with TensorRT-LLM, follow the sample commands below with the TensorRT-LLM GitHub repo:
- Checkpoint convertion:
python examples/llama/convert_checkpoint.py --model_dir Llama-3.1-8B-Instruct-FP8 --output_dir /ckpt --use_fp8
- Build engines:
trtllm-build --checkpoint_dir /ckpt --output_dir /engine
- Throughputs evaluation:
Please refer to the TensorRT-LLM benchmarking documentation for details.
Evaluation
We benchmarked with tensorrt-llm v0.13 on 8 H100 GPUs, using batch size 1024 for the throughputs with in-flight batching enabled. We achieved ~1.3x speedup with FP8.
Deploy with vLLM
To deploy the quantized checkpoint with vLLM, follow the instructions below:
- Install vLLM from directions here.
- To use a Model Optimizer PTQ checkpoint with vLLM,
quantization=modeloptflag must be passed into the config while initializing theLLMEngine.
Example deployment on H100:
from vllm import LLM, SamplingParams
model_id = "nvidia/Llama-3.1-8B-Instruct-FP8"
sampling_params = SamplingParams(temperature=0.8, top_p=0.9)
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
llm = LLM(model=model_id, quantization="modelopt")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
This model can be deployed with an OpenAI Compatible Server via the vLLM backend. Instructions here.