vLLM vs SGLang
Two of the top projects in Model serving, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
vLLM
Inference engine for Hugging Face LLMs with PagedAttention and continuous batching
SGLang
Inference engine for LLM, vision-language and diffusion models on many hardware types
Verdict
vLLM ranks higher, 88 to 82. Of the 4 score parts we measure for both, vLLM leads on 2, SGLang on 1 and 1 is even.
- AdoptionvLLM leads by 13 points, 96 to 83.
- FreshnessEven at 100.
- MaintenanceSGLang leads by 5 points, 85 to 80.
- Agent-readyvLLM leads by 30 points, 45 to 15: a wide gap.
Pick vLLM if you are
Engineers running high-throughput LLM inference on GPUs or other accelerators
- Continuous batching, chunked prefill, prefix caching and PagedAttention for high-throughput serving
- Wide quantization support: FP8, NVFP4, INT4/INT8, GPTQ/AWQ, GGUF and others
Pick SGLang if you are
Engineers serving LLMs or multimodal models in production or RL pipelines
- Supports NVIDIA, AMD, TPU, Intel, Apple Silicon, Ascend and Moore Threads hardware
- Built-in diffusion engine for image and video generation in the same package
| What we compare | vLLM | SGLang |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 83, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 80, healthy | 85, healthy |
| Agent-ready | 45, minimal | 15, none |
| Facts from GitHub and the README | ||
| Stars | 96k | 38.3k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
vLLM
vLLM is a Python library and server for LLM inference. It manages attention KV memory with PagedAttention, batches requests continuously, and exposes an OpenAI-compatible API, plus the Anthropic Messages API and gRPC. It supports 200+ Hugging Face model architectures and runs on NVIDIA, AMD and Intel GPUs, several CPU types, and other accelerators through plugins.
Who it is for: Engineers running high-throughput LLM inference on GPUs or other accelerators
Strengths
- Continuous batching, chunked prefill, prefix caching and PagedAttention for high-throughput serving
- Wide quantization support: FP8, NVFP4, INT4/INT8, GPTQ/AWQ, GGUF and others
- Tensor, pipeline, data, expert and context parallelism for multi-GPU inference
- Runs on NVIDIA, AMD, Intel GPUs and x86/ARM/PowerPC CPUs, with plugins for TPU, Gaudi, Ascend
Weaknesses
- README gives no minimum RAM or VRAM figures
- Several hardware targets depend on separate plugins rather than the core package
- Large feature surface means many tuning options to learn
- Server and library only, with no built-in chat UI
- GPU optional
- Docker
- Needs Python, PyTorch
- Models: Hugging Face models, Llama, Qwen, Gemma, Mixtral
SGLang
SGLang is an inference framework that serves large language, vision-language and diffusion models, aimed at agentic workloads, large-scale serving and RL rollouts. It includes SGLang Diffusion for image and video generation, and runs on NVIDIA, AMD, Google TPU, Intel, Apple Silicon, Huawei Ascend and Moore Threads hardware. A cookbook generates launch commands per model and hardware pair.
Who it is for: Engineers serving LLMs or multimodal models in production or RL pipelines
Strengths
- Supports NVIDIA, AMD, TPU, Intel, Apple Silicon, Ascend and Moore Threads hardware
- Built-in diffusion engine for image and video generation in the same package
- Integrates with RL training stacks such as verl and slime for rollouts
- Cookbook gives ready-to-run launch commands per model and hardware
Weaknesses
- README gives no RAM or VRAM requirements; sizing depends on model and hardware
- Several hardware integrations (Trainium, Cambricon, Qualcomm) are still in progress
- TTS and ASR serving live in a separate project, SGLang Omni
- Install uses --prerelease=allow, so dependencies may include prerelease packages
- GPU optional
- Docker + Compose
- Models: LLMs, vision-language models, diffusion models
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