TokenAssemble

Runtime comparison

vLLM vs SGLang

vLLM is a Apache-2.0-licensed runtime for Linux running on CUDA · ROCm; SGLang is Apache-2.0-licensed for Linux on CUDA · ROCm. Both rate 2/5 for ease of setup. Pick vLLM for serving many requests at once; pick SGLang for serving many requests at once.

SpecvLLMSGLang
LicenseApache-2.0Apache-2.0
Operating systemsLinuxLinux
GPU backendsCUDA · ROCmCUDA · ROCm
Installpip / sourcepip / source
Ease of use2/52/5
Quant formatsawq, gptqawq, gptq
Enginevllmsglang
GitHub stars85,77030,090
Latest versionv0.24.0v0.5.14
Graphical app (GUI)NoNo
Server modeYesYes
OpenAI-compatible APIYesYes
CPU offloadNoNo
Multi-GPUYesYes
Speculative decodingYesYes
LoRA supportYesYes
KV-cache quantizationYesYes

Which should you use?

vLLM

Best for serving many requests at once. Command line and server, runs on CUDA · ROCm.

Full vLLM guide →

SGLang

Best for serving many requests at once. Command line and server, runs on CUDA · ROCm.

Full SGLang guide →

Neither runtime changes whether a model fits — that is your memory and quantization. Check your hardware first, then pick the runtime.

Sources: vLLM github.com/vllm-project/vllm (as of 2026-07-08); SGLang github.com/sgl-project/sglang (as of 2026-07-08).