SGLang has emerged as the most serious challenger to vLLM for production LLM inference. On workloads with shared context — chatbots, RAG, agents — it delivers up to 29% higher throughput. The advantage comes from a different approach to KV cache management that makes prefix sharing automatic and aggressive.
How SGLang differs from vLLM
Both use PagedAttention for memory management and continuous batching for throughput. The key difference is how they handle shared prefixes.
SGLang introduces RadixAttention, a radix tree-based KV cache system. When multiple requests share the same prefix — a system prompt, document context, or few-shot examples — SGLang detects the overlap and computes the KV cache once. Subsequent requests reuse the cached prefix instantly.
vLLM supports prefix caching via --enable-prefix-caching, but SGLang’s radix tree enables fine-grained token-level matching that catches partial overlaps vLLM misses. The result is better cache hit rates on real-world workloads.
Benchmarks
| Workload | vLLM | SGLang | Improvement |
|---|---|---|---|
| Chatbot (shared system prompt) | 100 tok/s | 129 tok/s | +29% |
| RAG (shared documents) | 85 tok/s | 108 tok/s | +27% |
| Multi-turn conversation | 90 tok/s | 112 tok/s | +24% |
| Unique prompts (no sharing) | 100 tok/s | 102 tok/s | +2% |
The pattern is clear. Shared context yields substantial gains. Unique prompts yield near-identical performance. The advantage is almost entirely from prefix cache efficiency.
Architecture and philosophy
vLLM is a general-purpose engine with broad model support, extensive integrations, and a mature ecosystem. It prioritizes compatibility and reliability with over two years of production use at major companies.
SGLang is more opinionated. It is built around the idea that most production workloads have significant prefix sharing, and optimizing for that yields the biggest gains. Beyond RadixAttention, SGLang includes a domain-specific language for structured generation — branching, loops, and constrained output like JSON schemas during generation. This makes it particularly useful for function calling and structured data extraction.
When to use SGLang
SGLang is the better choice for chatbots where every request includes the same system prompt. The KV cache is computed once and shared across all users, saving significant compute.
RAG systems benefit because multiple queries often search the same documents. Shared document context is cached and reused.
AI agents with shared context windows see similar gains. When an agent processes multiple steps with the same project context, SGLang avoids recomputing the KV cache for the unchanged prefix.
Multi-user coding servers where developers share project context are another strong use case.
When to stick with vLLM
If your workload has unique prompts with no shared prefixes, the engines perform identically and vLLM’s larger ecosystem is an advantage.
Production stability matters. vLLM has been battle-tested longer with a more extensive track record. If you are running vLLM with no performance issues, migration cost may not justify a 25% improvement.
The ecosystem is larger — more integrations, deployment guides, community support, and tooling. For a broader comparison, see our vLLM vs Ollama vs llama.cpp guide.
Multi-LoRA serving is better supported in vLLM for serving multiple fine-tuned variants from one base model.
Model support
Both support major open-weight families — Llama, Qwen, Mistral, Gemma, DeepSeek. SGLang’s coverage has expanded rapidly but trails vLLM slightly on niche architectures. For mainstream models, both work equally well.
Both handle GPTQ, AWQ, and FP8 quantization. vLLM supports additional formats like SqueezeLLM. In practice, common quantization methods work on both without issues.
Quick start
# SGLang
pip install sglang[all]
python -m sglang.launch_server --model Qwen/Qwen3.5-27B --port 8000
# vLLM equivalent
pip install vllm
vllm serve Qwen/Qwen3.5-27B --port 8000Both expose OpenAI-compatible APIs, so Ollama-compatible clients, Aider, and any tool speaking the OpenAI format work without changes. Switching often requires changing only the launch command.
Performance tuning
SGLang’s RadixAttention benefits from larger KV cache allocations. The --mem-fraction-static flag controls GPU memory reserved for the cache. More cache means better hit rates and higher throughput on shared-context workloads.
For vLLM, enabling --enable-prefix-caching closes some of the gap with SGLang. Both engines benefit from tuning max batch size, tensor parallelism for multi-GPU setups, and quantization settings. Profile your specific workload before committing to either — the benchmarks above are averages, and your mileage will vary based on prompt length distribution and concurrency patterns.
FAQ
Is SGLang faster than vLLM?
SGLang is 24–29% faster on workloads with shared context — chatbots, RAG, agents. On unique prompts with no prefix sharing, the two perform nearly identically. The speed advantage comes from RadixAttention, which caches and reuses shared prefixes more aggressively than vLLM’s prefix caching.
Can I use SGLang with Ollama?
They serve different purposes and do not integrate directly. Ollama is for local single-user inference using llama.cpp. SGLang is a production serving engine for GPU-based multi-user inference. Both expose OpenAI-compatible APIs, so your application code works with either — but you would not run them together.
Which is better for production?
vLLM currently has the edge due to its longer track record, larger ecosystem, and broader community. SGLang is catching up and is used in production by several companies. If your workload has significant prefix sharing and you want maximum throughput, SGLang is worth evaluating. If you prioritize stability and ecosystem maturity, vLLM is safer.
Does SGLang support all models?
SGLang supports all major open-weight families — Llama, Qwen, Mistral, Gemma, DeepSeek, Phi. Coverage of niche or very new architectures may lag behind vLLM since vLLM has a larger contributor base. For mainstream production models, both provide full support. Both engines require capable GPUs — if you don’t have local hardware, cloud GPU providers offer A100s and H100s on demand for production inference.