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).

Results

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 | 3.78737 | 36.1465 | 29.8414 | 98.9095 | 12.7744 | 12.1359 | 38.6988 | lmdeploy | | lmdeploy_llama8B_fp8_tp1_qps_8 | A100-SXM4-80GB | 500 | 7.04898 | 40.947 | 35.3738 | 99.8898 | 14.2108 | 12.991 | 54.4581 | lmdeploy |

Performance Benchmark

reduce calib size