SGLang vs vLLM (2026): Features, Performance, Hardware, and Which to Pick

TL;DR

Last updated September 30, 2026.

SGLang and vLLM are the two most used open-source LLM serving engines. Both are Apache 2.0 and both run an OpenAI-compatible server with continuous batching, prefix caching, speculative decoding, structured outputs, and multi-GPU parallelism. Pick SGLang when requests share long prefixes (agent loops, multi-turn chat, RL rollouts), because RadixAttention reuses that KV cache automatically. Pick vLLM when you want the widest model and hardware plugin coverage, multi-LoRA, or Anthropic Messages API compatibility. Current releases are vLLM v0.30.0 (September 22, 2026) and SGLang v0.5.20 (September 18, 2026).

SGLang vs vLLM Comparison Table

Every row below comes from the two projects' own READMEs, documentation, and GitHub release pages, checked September 30, 2026.

PagedAttention vs RadixAttention

vLLM's PagedAttention, described in the 2023 paper "Efficient Memory Management for Large Language Model Serving with PagedAttention" (Kwon et al.), treats the KV cache like virtual memory. It splits each sequence's cache into fixed-size blocks that do not have to be contiguous, so the server wastes almost no GPU memory on fragmentation and can fit more concurrent sequences into a batch.

SGLang's RadixAttention, introduced in the LMSYS post of January 17, 2024, solves a different problem. Instead of discarding a request's KV cache when it finishes, SGLang keeps it in a radix tree keyed by token sequence. When a new request arrives, the scheduler matches its longest cached prefix and only computes the remainder. Multi-turn chat, a shared system prompt, few-shot examples, and tree-of-thought search all reuse cache this way without any manual configuration.

SGLang vs vLLM Performance and Benchmarks

LMSYS's launch benchmark reported SGLang reaching up to 5x the throughput of vLLM and Guidance on Llama-7B and Mixtral-8x7B on NVIDIA A10G GPUs, measured on chained LLM tasks with heavy prefix sharing. That number is from January 2024 and from the SGLang authors, so read it as the upper bound for prefix-heavy work, not a general result. vLLM has added prefix caching, CUDA graphs, torch.compile kernel generation, and disaggregated prefill and decode since then.

Benchmark on your own traffic. Throughput between the two engines swings with the model, the GPU, batch size, input and output lengths, and above all how much prefix your requests share. Both projects document a benchmarking workflow for replaying a request mix against a running server.

Hardware Support

APIs and Launch Commands

Both servers accept requests from the standard OpenAI client, so switching between them is mostly a base URL change.

# vLLM: OpenAI-compatible server on http://localhost:8000/v1
uv pip install vllm
vllm serve Qwen/Qwen3-4B

# SGLang: OpenAI-compatible server on http://localhost:30000/v1
uv pip install --prerelease=allow sglang
python3 -m sglang.launch_server --model-path Qwen/Qwen3-4B --host 0.0.0.0

vLLM's server also implements the Anthropic Messages API, so tools that expect an Anthropic endpoint can point at a self-hosted model without a proxy. Both engines ship tool-calling and reasoning parsers per model family; a mismatched parser is the most common reason tool calls come back as plain text.

RL Rollouts and Agent Workloads

SGLang describes itself as optimized for agentic workloads, RL rollouts, and large-scale serving. Its README lists Miles, slime, AReaL, Tunix, and verl as training frameworks that call SGLang for rollout generation. RL sampling draws many completions from one prompt, and agent loops resend a growing transcript every step, so both reuse long prefixes on almost every request.

vLLM covers the same ground with RLHF examples for weight sync over HTTP, IPC, and NCCL. For a broader view of where engine choice sits among the other levers, see LLM inference optimization, continuous batching, speculative decoding, and prompt caching. To see how a minimal engine is put together, read nano-vLLM.

SGLang vs vLLM Pros and Cons

Which One to Pick

Start from the shape of your traffic. If most requests share a long system prompt or transcript, or you are generating RL rollouts, run SGLang first. If you serve many different models, need dozens of LoRA adapters, depend on an accelerator that only has a vLLM plugin, or want an Anthropic-compatible endpoint, run vLLM first. In every case, replay a day of real requests against both before you standardize, since the right answer moves with each release.

If you would rather not operate either engine, Morph serves open models on its own kernels through an OpenAI-compatible API. See Morph Open Source Models and the dedicated inference calculator.

Frequently Asked Questions

Sources

• vLLM GitHub README (features, hardware, install)
• vLLM v0.30.0 release, September 22, 2026
• Kwon et al., Efficient Memory Management for LLM Serving with PagedAttention
• SGLang GitHub README (hardware, ecosystem, install)
• SGLang v0.5.20 release, September 18, 2026
• LMSYS: Fast and Expressive LLM Inference with RadixAttention and SGLang
• SGLang documentation (OpenAI-compatible APIs)