SGLang
An open-source LLM serving engine optimized for structured generation and prefix sharing. Distributed under Apache 2.0. The **RadixAttention** mechanism — SGLang's core innovation — uses a radix tree to identify and share KV-cache state across requests with overlapping prefixes, dramatically improving throughput for workloads where prompts share large structured prefixes (system instructions, few-shot examples, persistent context). RadixAttention `depends_on` remote storage backends for evicting cold cache lines, making S3 the natural durability target.
Definition
An open-source LLM serving engine optimized for structured generation and prefix sharing. Distributed under Apache 2.0. The **RadixAttention** mechanism — SGLang's core innovation — uses a radix tree to identify and share KV-cache state across requests with overlapping prefixes, dramatically improving throughput for workloads where prompts share large structured prefixes (system instructions, few-shot examples, persistent context). RadixAttention `depends_on` remote storage backends for evicting cold cache lines, making S3 the natural durability target.
Most LLM serving engines treat each request as independent, recomputing the entire KV-cache for every prompt. Production workloads have massive prefix overlap — multi-tenant serving with shared system prompts, agentic workflows with persistent context, structured generation with template prefixes. SGLang's RadixAttention specifically exploits this redundancy at the engine level, with eviction to remote storage so the radix tree doesn't bound itself to GPU RAM.
Structured generation with high prefix overlap, multi-tenant LLM serving, agentic workflows with persistent context, function-calling pipelines with shared schema prefixes.
Recent developments
- Latest release: v0.5.14 (current as of June 2026). Tracking the upstream stable release line. Per sgl-project/sglang releases.
- SGLang v0.5.8 (January 2026) shipped; deployed on 400,000+ GPUs worldwide. The de facto industry-standard inference engine alongside vLLM and TensorRT-LLM. Per Wikipedia — SGLang and Inference.net — SGLang Complete Guide.
- RadixAttention: 6.4× throughput vs vLLM on RAG + multi-turn. SGLang's core innovation — a radix tree (trie-like LRU) of KV cache shared across all concurrent requests. Delivers 6× acceleration in RAG scenarios and ~29% lead over vLLM on H100s for structured prefix workloads. Per ChatForest — SGLang 2026 Review.
- Joined the PyTorch ecosystem (March 2025). SGLang is now an official PyTorch ecosystem project, signaling first-class long-term support. Per Wikipedia — SGLang.
- Now integrated with LMCache as a cross-engine KV-cache offloader. SGLang + LMCache lets the radix-tree state evict to CPU memory / NVMe / S3 — the radix tree no longer bounds itself to GPU RAM. Pairs SGLang's prefix-sharing with LMCache's persistence layer. Per arXiv — LMCache paper.
- Structured generation is the workload it's best at. SGLang's secondary differentiator is grammar-constrained / schema-constrained output (JSON, tool calls, regex). When the prefix carries a system prompt + tool schema + few-shot examples, RadixAttention's benefit compounds with structured-generation throughput wins. Per Runpod — SGLang in Production: Structured Generation, RadixAttention.
- 2026 head-to-head: vLLM vs SGLang vs TensorRT-LLM vs Ollama landscape settles. TheAIEngineer's benchmark report places SGLang as the leader on RAG + multi-turn + prefix-heavy workloads; vLLM wins general purpose; TensorRT-LLM wins peak throughput on NVIDIA; Ollama wins dev-loop ergonomics. The four-way market is shaped, not consolidating. Per TheAIEngineer — vLLM vs Ollama vs SGLang vs TensorRT-LLM 2026.
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