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SGLang

by LMSYS

High-throughput LLM serving framework with RadixAttention KV cache reuse and grammar-constrained structured output — enabling APAC AI teams to serve JSON-mode, function calling, and multi-call LLM workflows at 3–5× higher throughput than standard vLLM for structured generation workloads.

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AIMenta verdict
Recommended
5/5

"High-throughput LLM serving for APAC teams — SGLang accelerates structured LLM generation with RadixAttention KV cache sharing and grammar-constrained decoding, enabling APAC teams to serve structured output APIs (JSON, function calling) at 3-5× higher throughput than vLLM."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • RadixAttention: APAC automatic KV cache reuse for shared prompt prefixes
  • Structured output: APAC grammar-constrained JSON/regex generation without failures
  • OpenAI API: APAC drop-in replacement for vLLM or OpenAI serving endpoints
  • Function calling: APAC guaranteed tool call schema compliance
  • Multi-modal: APAC vision-language model serving (LLaVA, Qwen-VL)
  • Speculative decoding: APAC draft model acceleration for generation throughput
When to reach for it

Best for

  • APAC AI teams serving LLM APIs with structured output requirements (JSON extraction, function calling, agent tool schemas) at scale — particularly APAC multi-tenant LLM API providers where shared system prompt caching via RadixAttention delivers significant throughput improvements over per-request KV cache allocation.
Don't get burned

Limitations to know

  • ! APAC RadixAttention benefits diminish for workloads with highly variable or unique prompts
  • ! Newer project than vLLM — APAC community and enterprise support surface is smaller
  • ! APAC CUDA/GPU only — no CPU inference support unlike llama.cpp
Context

About SGLang

SGLang (Structured Generation Language) is an open-source LLM serving framework from LMSYS that achieves 3–5× higher throughput than vLLM for structured generation workloads through two key innovations: RadixAttention for automatic KV cache reuse across shared prompt prefixes, and grammar-constrained decoding for guaranteed structured output generation. APAC AI teams serving LLM APIs with structured output requirements (JSON extraction, function calling, multi-turn agents with shared system prompts) use SGLang as their inference backend to dramatically increase GPU throughput without hardware upgrades.

SGLang's RadixAttention reuses KV cache across requests that share a common prefix — in APAC multi-tenant LLM APIs where all requests share the same system prompt and organization-specific context, RadixAttention caches that shared prefix once and reuses it across all concurrent requests. APAC LLM API providers serving 100+ concurrent APAC enterprise users with identical system prompts see GPU memory utilization improve dramatically, allowing more concurrent requests per GPU and lower cost per token served.

SGLang's grammar-constrained decoding guarantees structured output that matches a specified JSON schema or regex pattern — LLM output is guided token-by-token to stay within the valid grammar, eliminating JSON parsing failures that require expensive retry calls. APAC applications that parse LLM JSON output for downstream processing (structured data extraction, API response formatting, agent tool call schemas) eliminate retry loops and reduce end-to-end latency by replacing probabilistic JSON generation with guaranteed grammar-constrained output.

SGLang's OpenAI-compatible API server allows APAC teams to migrate from vLLM or OpenAI API endpoints without application code changes — the same client code that calls `/v1/chat/completions` works against SGLang's server with structured output mode enabled via the `response_format` parameter. APAC teams running self-hosted LLM infrastructure can switch inference backends from vLLM to SGLang by changing the server startup command, immediately gaining throughput improvements on structured generation workloads without application changes.

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