Open liho00 opened 4 days ago
It looks like you are running the FP16 model in your launch command
That being said, you are running a 3b model with tp=8. I do not think you will see much performance benefit from fp8 in this regime since the linear layers are very small in this setup
It looks like you are running the FP16 model in your launch command
That being said, you are running a 3b model with tp=8. I do not think you will see much performance benefit from fp8 in this regime since the linear layers are very small in this setup
Sorry for the typo, its should be 8b model Llama-3.1-8B-Instruct-FP8
python -m vllm.entrypoints.openai.api_server --served-model-name Llama-3.1-8B-Instruct-FP8 --model /root/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 --port 8000 --host 0.0.0.0 --tensor-parallel-size 8 --gpu-memory-utilization 0.95 --dtype bfloat16 --quantization compressed-tensors
any idea to speed up with vllm for compressed the model? the ideal will be having low latency for the first token.
Or it only speed up for large model for 70b only?
One last question - is this running on an H100?
One last question - is this running on an H100?
yeap, 8xh100 smx5,
can I add you in discord for further details sharing?
One last question - is this running on an H100?
yeap, 8xh100 smx5,
can I add you in discord for further details sharing?
With an 8xh100, your system is very overpowered for running an 8b parameter model, so the e2e speedup from quantization is small (and we have not really tuned the fp8 kernels for matrices that are so skinny).
I would expect to see speedups on a 1xh100 for an 8b parameter scale though
https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_w8a8_fp8
https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_w8a8_int8
https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_w4a16
i tried these example code to generate a new compressed checkpoint and load with vllm 0.6.3
python -m vllm.entrypoints.openai.api_server --served-model-name /home/llm-compressor/examples/quantization_w8a8_fp8/Llama-3.1-8B-Instruct-FP8 --model meta-llama/Llama-3.1-8B-Instruct --port 8000 --host 0.0.0.0 --tensor-parallel-size 8 --gpu-memory-utilization 0.98
base model: 215 tok/s compressed model: 205 tok/s