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.

Workload description

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, 8 for 8B model and 1, 4 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)	Median TTFT (ms)	P99 TTFT (ms)	Mean ITL (ms)	Median ITL (ms)	P99 ITL (ms)	Engine
lmdeploy_llama8B_fp8_tp1_qps_4	A100-SXM4-80GB	500	3.78675	36.1998	30.3184	92.0275	12.8315	12.1981	38.081	lmdeploy
lmdeploy_llama8B_fp8_tp1_qps_8	A100-SXM4-80GB	500	7.04438	41.0228	36.1189	94.2032	14.4496	13.2231	53.3248	lmdeploy