Nightly benchmark

The main goal of this benchmarking is two-fold:

Performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and tgi) leads in performance in what workload.
Reproducible: one can run the exact same set of benchmarking commands inside the exact same docker by following reproducing instructions in reproduce.md.

Versions

We benchmark vllm, tensorrt-llm, lmdeploy and tgi using the following docker images:

Check nightly-pipeline.yaml artifact for more details.

We benchmark vllm, tensorrt-llm, lmdeploy and tgi using the following workload:

Input length: randomly sample 1000 prompts from ShareGPT dataset (with fixed random seed).
Output length: the corresponding output length of these 1000 prompts.
Batch size: dynamically determined by vllm and the arrival pattern of the requests.
Average QPS (query per second): 4 for 8B model and 2 for larger models. For each QPS, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
Models: llama-3 8B, llama-3 70B, mixtral 8x7B.
Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).

Test name	GPU	Successful req.	Tput (req/s)	Mean TTFT (ms)	Std TTFT (ms)	Mean ITL (ms)	Std ITL (ms)	Engine
tgi_llama8B_tp1_qps_4	A100-SXM4-80GB	500	3.74424	113.169	104.848	16.8181	8.43854	tgi
tgi_llama8B_tp1_qps_8	A100-SXM4-80GB	500	6.8245	267.488	153.048	24.046	16.7538	tgi