README - Sglang — ContextQMD

How to reproduce the benchmark results for SGLang v0.3.0 compared to vLLM v0.6.0

In short, with multi step enabled, in online scenarios that we benchmarked, the Median TTFT of vLLM is 3 times that of SGLang, and the Median ITL is 10 times that of SGLang. Lower Median TTFT and ITL are better. vLLM's multi-step optimization did not improve throughput while ensuring lower Median TTFT and ITL. Also, under maximum throughput benchmark, if vLLM does not set gpu util to 0.95 separately and uses the default configuration instead, its maximum throughput is lower than that of SGLang.

Online benchmark results

Llama 3.1 8B Instruct 1 x A100 80G

RPS	Num prompts	Engine	Median E2E Latency	Median TTFT	Median TPOT	Median ITL
4	1200	SGLang	1564.17	31.98	13.17	11.93
4	1200	vLLM	1691.97	100.48	14.14	129.32
8	2400	SGLang	2175.02	35.68	17.85	14.41
8	2400	vLLM	2137.16	120.39	17.09	158.63

Llama 3.1 70B Insruct 4 x H100 80G

RPS	Num Prompts	Engine	Median E2E Latency	Median TTFT	Median TPOT	Median ITL
4	1200	SGLang	3005.24	53.94	25.03	21.67
4	1200	vLLM	2915.60	179.15	23.58	231.23
8	2400	SGLang	4064.98	58.11	33.07	24.45
8	2400	vLLM	3752.38	207.12	29.15	275.32

Offline benchmark results

Llama 3.1 8B Instruct 1 x A100 80G

RPS	Num Prompts	Engine	Request throughput	Output token throughput
inf	5000	SGLang	22.03	4281.51
inf	5000	vLLM	21.27	4132.37

Llama 3.1 70B Insruct 4 x H100 80G

RPS	Num Prompts	Engine	Request throughput	Output token throughput
inf	5000	SGLang	19.84	3856.01
inf	5000	vLLM	19.04	3700.64

Installation

bash

# install sglang v0.3.0
pip install --upgrade pip
pip install "sglang[all]"==0.3.0
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/

# install vllm v0.6.0
pip install vllm==0.6.0

Notes

We referred to the reproduction method in https://github.com/vllm-project/vllm/issues/8176, and added the --num-scheduler-steps 10 parameter when starting the vLLM server. The gpu_memory_utilization of vLLM is by default 0.9 at both TP 1 and TP 4, while SGLang's mem_frac is 0.88 at TP 1 and 0.85 at TP 4, so we manually set it to 0.88 at TP 4.

Online benchmarks

bash

# Llama 3.1 8B Instruct on 1 x A100
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --enable-torch-compile --disable-radix-cache
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.1-8B-Instruct --disable-log-requests --num-scheduler-steps 10 --max_model_len 4096

# Llama 3.1 70B Instruct on 4 x H100
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-70B-Instruct --disable-radix-cache --tp 4
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.1-70B-Instruct --disable-log-requests --num-scheduler-steps 10 --tensor 4 --max_model_len 4096

# bench serving
python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompts 1200 --request-rate 4
python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompts 2400 --request-rate 8
python3 -m sglang.bench_serving --backend vllm --dataset-name sharegpt --num-prompts 1200 --request-rate 4
python3 -m sglang.bench_serving --backend vllm --dataset-name sharegpt --num-prompts 2400 --request-rate 8

Offline benchmarks

bash

# Llama 3.1 8B Instruct on 1 x A100
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --enable-torch-compile --disable-radix-cache
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.1-8B-Instruct --disable-log-requests --num-scheduler-steps 10 --max_model_len 4096

# Llama 3.1 70B Instruct on 4 x H100
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-70B-Instruct --disable-radix-cache --tp 4 --mem-frac 0.88
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.1-70B-Instruct --disable-log-requests --num-scheduler-steps 10 --tensor 4 --max_model_len 4096

# bench serving
python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompts 5000
python3 -m sglang.bench_serving --backend vllm --dataset-name sharegpt --num-prompts 5000