- vLLM is the safer default: broadest model and hardware support, biggest ecosystem, least deployment friction.
- Pick SGLang when your traffic reuses prompt prefixes heavily (agents, multi-turn chat, big system prompts) or produces lots of structured JSON output — RadixAttention and jump-forward decoding win there.
- The performance gap is workload-dependent and shrinks with every release. Both speak the OpenAI API, so benchmarking one against the other on your real traffic is cheap.
- Neither runs natively on Windows or macOS — use Linux (or WSL2), or a desktop tool like Ollama/LM Studio for local use.
vLLM and SGLang are the two leading open-source engines for serving large language models on your own GPUs, and for most teams vLLM is the safer default: more models, more hardware backends, a larger ecosystem. Choose SGLang when your traffic is dominated by shared prompt prefixes — agents, multi-turn chat, heavy system prompts — or by structured JSON output, where its RadixAttention cache and grammar-based decoding give it a real edge. Both expose an OpenAI-compatible API, so switching later costs almost nothing.
This guide compares them the way you would actually choose: what each project optimises for, how RadixAttention and PagedAttention differ in practice, where each wins on throughput and latency, and a clear recommendation by workload.
What Each Project Optimises For
vLLM started at UC Berkeley in 2023 as the reference implementation of the PagedAttention paper and has since become the de facto standard open serving engine, now a PyTorch Foundation project. Its priorities are breadth and robustness: run nearly every open-weight model on nearly every accelerator — NVIDIA CUDA, AMD ROCm, Intel, Google TPU, AWS Neuron, even x86 CPU — with strong throughput out of the box. New model families usually get vLLM support on or near day one.
SGLang comes from the LMSYS team behind Chatbot Arena. It optimises for two specific things: KV-cache reuse (RadixAttention) and fast structured generation. The name is short for Structured Generation Language — it originally shipped with a Python DSL for chaining and branching LLM calls — but the serving runtime is what most people deploy today. It has serious production credentials: it was among the engines DeepSeek recommended at the V3 launch, and it is known for aggressive multi-GPU optimisations (prefill/decode disaggregation, large-scale expert parallelism for MoE models).
RadixAttention vs PagedAttention, in Plain Terms
The two headline techniques solve different problems, and the names invite a false either/or.
PagedAttention (vLLM) is memory management. It stores the KV cache in fixed-size blocks, like an operating system pages virtual memory, instead of reserving one big contiguous chunk per request. That nearly eliminates fragmentation, so far more concurrent sequences fit in the same VRAM, and bigger batches mean higher throughput. It is about fitting more in.
RadixAttention (SGLang) is memory reuse. It organises the KV cache as a radix tree keyed by token sequences. When a new request arrives, the engine walks the tree, finds the longest matching prefix, and skips recomputing it. System prompts, few-shot examples, conversation history, and agent scratchpads that repeat across requests are prefill-computed once and reused. It is about computing less.
In 2026 the two engines have converged more than the branding suggests: vLLM has automatic prefix caching (enabled by default in recent versions), and SGLang also pages its memory. The remaining practical difference is that SGLang was designed around reuse from day one — its scheduler is cache-aware, ordering and routing requests to maximise hit rate. The rule of thumb: the larger the share of your typical prompt that repeats across requests, the more SGLang’s design pays off.
Either way, the KV cache competes with model weights for the same GPU memory, and concurrency headroom is what a serving engine actually buys you. Use the VRAM calculator to estimate how much memory a given model plus context leaves for cache before you size hardware.
Throughput and Latency: Where Each Wins
| Workload | Typically faster | Why |
|---|---|---|
| Multi-turn chat, agent loops, large shared system prompts | SGLang | RadixAttention skips re-prefilling repeated prefixes, cutting time-to-first-token and freeing compute |
| One-off prompts with little overlap (unique documents, batch summarisation) | Roughly even | Both use continuous batching and chunked prefill; cache reuse rarely triggers |
| High-volume JSON / constrained output | SGLang | Jump-forward decoding emits grammar-forced tokens without a model forward pass |
| Mixed model zoo, exotic quantisation, non-NVIDIA hardware | vLLM | Broader backend and format coverage means fewer unoptimised fallback paths |
Treat any specific throughput numbers you find online as version-bound. Both projects ship optimisations continuously, and each has published benchmarks where it beats the other. The honest answer is that on cache-friendly traffic SGLang usually leads, on cold-cache traffic they are close, and the only benchmark that matters is your own: both ship load-testing tools (vLLM’s vllm bench serve, SGLang’s python -m sglang.bench_serving) that replay realistic request streams.
Structured and Constrained Output
Both engines can force output to match a JSON schema, regular expression, or grammar, exposed through the OpenAI-style response_format parameter plus engine-specific extensions.
SGLang pioneered the fast path here: it compiles constraints into a compressed finite-state machine and uses jump-forward decoding — when the grammar makes the next several tokens deterministic (braces, quotes, fixed key names), it appends them directly instead of running the model for each one. For extraction pipelines producing token-heavy JSON, that is a meaningful speedup.
vLLM supports the same class of constraints through pluggable grammar backends (xgrammar, guidance, outlines), and since both projects adopted xgrammar as a default backend the gap has narrowed considerably. Verdict: both are production-ready; SGLang retains an edge on heavily structured, high-volume workloads.
Ecosystem, Hardware Support and Deployment Friction
| vLLM | SGLang | |
|---|---|---|
| Origin | UC Berkeley; PyTorch Foundation project | LMSYS (Chatbot Arena team) |
| Hardware | NVIDIA, AMD ROCm, Intel, Google TPU, AWS Neuron, x86 CPU | NVIDIA first-class, AMD ROCm supported; others less mature |
| Model coverage | Widest of any engine, including many multimodal and niche architectures | All major families (Llama, Qwen, DeepSeek, Mistral, GPT-OSS…), shorter tail |
| Quantisation | FP8, AWQ, GPTQ, INT8, bitsandbytes, more | FP8, AWQ, GPTQ; narrower list |
| API | OpenAI-compatible, default port 8000 | OpenAI-compatible, default port 30000 |
| Docker image | vllm/vllm-openai | lmsysorg/sglang |
Getting started is a one-liner for each on a CUDA Linux box:
pip install vllm then vllm serve Qwen/Qwen2.5-7B-Instruct — serves an OpenAI-compatible API on port 8000.
pip install "sglang[all]" then python -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct — same API shape on port 30000.
Multi-GPU tensor parallelism is --tensor-parallel-size 2 in vLLM and --tp 2 in SGLang. Both pull weights straight from Hugging Face. For picking the card itself, see the guide to the best GPUs for local LLMs and check individual model requirements in the models database.
Where friction differs: vLLM’s documentation, Kubernetes tooling, and community answers are simply more numerous — obscure errors are more likely to have a solved GitHub issue. SGLang’s installs are occasionally pickier about CUDA/PyTorch version combinations; the Docker image is the low-friction path.
Platform Support
Linux
The only first-class platform for both. NVIDIA GPUs with a recent CUDA-capable driver are the mainline path; AMD ROCm works on both with supported cards. Use the official Docker images for the least version pain.
Windows
Neither engine supports Windows natively. Both run under WSL2 with NVIDIA’s WSL CUDA driver, and that setup is fine for development. For production, use a real Linux host. If you just want a local model on a Windows desktop rather than a serving endpoint, Ollama is the simpler tool.
macOS
No GPU serving on Apple Silicon from either project. vLLM can be built from source for CPU-only inference on macOS, but that is a development convenience, not a deployment target; SGLang does not target macOS at all. For Mac-local inference, use Ollama or LM Studio, which use Apple’s Metal GPU acceleration.
Which to Pick, by Workload
- Chatbots, assistants, agent frameworks — long system prompts, tools, multi-turn history: SGLang. This is exactly the traffic RadixAttention was built for.
- High-volume structured extraction — classify/extract to JSON at scale: SGLang, for jump-forward decoding.
- Batch processing of unique documents — summarisation, embeddings-adjacent pipelines with little prompt overlap: vLLM; cache reuse won’t help, and vLLM’s tooling is deeper.
- Many different models, or non-NVIDIA hardware — AMD, Intel, TPU, Inferentia, exotic quantised checkpoints: vLLM, no contest on coverage.
- You’re not sure: start with vLLM, then A/B SGLang against it with your real request logs. The shared OpenAI API makes the swap a base-URL change.
And before committing to either, sanity-check that self-hosting beats just calling an API at your volume — the self-hosting vs API break-even calculator does that math per model and request load.
Frequently Asked Questions
Is SGLang faster than vLLM?
On workloads with heavy prefix reuse or structured output, usually yes — sometimes substantially. On cold-cache, one-off prompts the two are close, and results flip between releases. Benchmark with your own traffic pattern rather than trusting any single published number.
Can I use the OpenAI Python SDK with both?
Yes. Both serve an OpenAI-compatible /v1/chat/completions endpoint, so pointing the official SDK’s base_url at http://localhost:8000/v1 (vLLM) or http://localhost:30000/v1 (SGLang) works with any placeholder API key. This is what makes A/B testing them nearly free.
Doesn’t vLLM have prefix caching too?
It does — automatic prefix caching, on by default in recent versions, reuses KV blocks whose hashed content matches. SGLang’s radix tree matches at finer granularity and its scheduler actively orders requests to raise the hit rate, which is why SGLang still tends to lead on cache-heavy traffic.
Which engine supports more models?
vLLM, clearly — its supported-architecture list is the longest of any open engine, especially for multimodal and niche models. SGLang covers every major open-weight family and often lands day-one support for flagship releases (its DeepSeek optimisations are notably strong), but the long tail belongs to vLLM. Check what a given model needs in the VRAM requirements guide.
Where do Ollama and llama.cpp fit in?
Different category. Ollama and LM Studio are single-user local tools optimised for convenience on desktops, including Macs; SGLang and vLLM are high-concurrency server engines optimised for GPU throughput across many simultaneous requests. If you’re serving one user, use Ollama; if you’re serving an application, use one of these two.
