SGLang

What SGLang actually is

SGLang is a structured-generation language and serving runtime for LLM programs built around the insight that agentic workloads share enormous amounts of prefix between calls. The runtime's signature contribution is RadixAttention — a KV-cache reuse scheme that automatically detects shared prefixes across requests and reuses cached attention states instead of recomputing them.

For workloads where the same long system prompt + tool list + few-shot exemplars is sent on every request — i.e., most production agent stacks in 2026 — RadixAttention turns repeated prefill work into roughly free throughput. On those workloads SGLang routinely outperforms vLLM, sometimes by 2-3× depending on prefix overlap density.

If vLLM is the production-default for general LLM serving, SGLang is the production-default specifically for prefix-cache-heavy agent serving. Many teams run both: vLLM for the chat endpoint, SGLang for the agent endpoint.

Where it fits in the stack

SGLang lives at the server layer, same as vLLM, with the same client integration shape (OpenAI-compatible HTTP API):

• Frontend / clients: Open WebUI, LibreChat, agent frameworks like LangChain / LlamaIndex
• Server: SGLang server on http://host:30000 (default port differs from vLLM)
• Engine: SGLang's own CUDA kernels + RadixAttention + FlashInfer integration
• Hardware: NVIDIA CUDA primary, AMD ROCm partial, Intel Gaudi unsupported

It is not the right layer for general single-shot chat with no shared prefix (vLLM is fine and simpler) or for laptops (use Ollama). It is the right layer when you have many tool-calling agents with long shared system prompts, or many RAG queries with the same retrieved-context preamble.

Best use cases

• Multi-agent coding stacks. When OpenHands or CrewAI-style agents make 30+ tool-calls per task with the same system prompt, RadixAttention dominates.
• Production RAG with deduplicated context. Same retrieved chunks reused across user turns? Free throughput.
• Structured output at scale. SGLang's grammar-constrained generation is competitive with Outlines and bakes into the runtime, not a wrapper.
• Reasoning-model serving. Long chain-of-thought traces with shared system prompts benefit hugely from prefix reuse.
• High-concurrency function-calling endpoints. Tool-using agents, browse-the-web stacks, anything that hits the model with structured prompts repeatedly.

OS support

Same Linux-or-bust posture as vLLM. The two projects are aimed at the same operator persona.

Hardware / runtime support

• NVIDIA CUDA — first-class. Ampere through Blackwell.
• AMD ROCm — supported on MI300X; RDNA3 consumer support lags vLLM.
• Intel Gaudi — not supported as of 2026.
• Multi-GPU — full tensor-parallel; pipeline-parallel for large models.
• NVLink-aware — picks up NVLink topology when available; matters for H100 SXM clusters.

For the multi-GPU strategy framing see /guides/running-local-ai-on-multiple-gpus-2026.

Model / quant format support

Tracks vLLM closely but with its own kernel paths:

• FP16 / BF16 — reference
• AWQ-INT4 — the consumer-card production sweet spot
• GPTQ-INT4 — supported
• FP8 — Hopper / Blackwell support; the datacenter quant
• GGUF — partial; not the recommended path
• EXL2 — unsupported

See /systems/quantization-formats for the cross-runtime view.

Setup path

Docker is the production path:

docker run --gpus all -p 30000:30000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --shm-size=10g \
  lmsysorg/sglang:latest \
  python3 -m sglang.launch_server \
  --model-path casperhansen/llama-3.1-8b-instruct-awq \
  --host 0.0.0.0 --port 30000

The pip path:

pip install "sglang[all]"
python -m sglang.launch_server --model-path <model> --port 30000

The OpenAI-compatible endpoint shape means most existing clients work with a base-URL change.

What breaks first

1. --shm-size too low. SGLang's shared-memory IPC needs a generous /dev/shm; the Docker default 64M will silently throttle throughput.
2. Prefix-cache eviction at high concurrency. RadixAttention's win shrinks if cache pressure is too high; tune --mem-fraction-static deliberately.
3. Unsupported model architectures. New model families land in vLLM first historically; SGLang catches up but lags by 2-6 weeks.
4. Multi-node deployment. Single-node TP works well; multi-node is rougher than vLLM's pipeline-parallel story.
5. AMD ROCm consumer cards. RDNA3 path exists but is the least-tested combination; default to vLLM if your hardware is consumer AMD.

Alternatives by intent

Best pairings

• Multi-agent stacks with LangGraph / CrewAI — the canonical RadixAttention win
• Open WebUI with shared system prompts across many users
• H100 SXM cluster — the canonical datacenter pairing
• RTX 4090 + AWQ-INT4 32B — the homelab agent-server default
• AnythingLLM workspace deployments where the same docs anchor every query

Who should avoid SGLang

• Single-user laptops. Overkill; use Ollama.
• Workloads with no prefix overlap. No RadixAttention win; use vLLM for simpler ops.
• Apple-only operators. Wrong stack; use MLX-LM.
• Teams that need bleeding-edge model architecture support on day one. vLLM lands new architectures first more often than not.

Related

• Stacks: /stacks/local-coding-agent, /stacks/dual-4090-workstation
• System guides: /systems/quantization-formats, /guides/running-local-ai-on-multiple-gpus-2026
• Hardware: RTX 4090, H100 SXM, RTX 5090
• Errors: /errors/rocm-device-not-found, /errors/wsl2-gpu-not-detected

Install via pip: pip install "sglang[all]". Requires Python 3.10+ and CUDA 12.1+ for NVIDIA GPU. Start the server with python -m sglang.launch_server --model meta-llama/Llama-3.1-8B-Instruct --port 30000. The server exposes an OpenAI-compatible /v1/chat/completions endpoint at port 30000 by default. Verify it's alive: curl http://localhost:30000/health. For constrained generation (JSON schema, regex), use the SGLang-native frontend language rather than the OpenAI endpoint — it compiles structural constraints into the token sampler. For multi-GPU: --tp-size 2 for tensor parallelism across two GPUs. First run downloads model weights from HuggingFace; a 70B model takes 10–25 minutes on a good connection. Time-to-first-request from zero with Docker: docker run --gpus all -p 30000:30000 lmsysorg/sglang:latest python -m sglang.launch_server --model meta-llama/Llama-3.1-8B-Instruct. SGLang's RadixAttention prefix tree compiles on first request and then persists — budget extra 3–5 seconds on first call with long system prompts.

Best for: high-volume LLM API serving with shared prefix structures (multi-turn chat, agent loops, RAG with common system prompts), structured generation workloads requiring guaranteed JSON-schema or regex-constrained output, evaluation pipelines that benchmark the same prompt template across many inputs, LMSYS-style chatbot arena serving where system prompts are reused across thousands of user turns. Not suited for: single-user desktop use (SGLang's value is in the scheduler, not the single-stream experience), CPU-only or non-NVIDIA GPU (CUDA-only as of mid-2026), workloads with highly diverse and non-overlapping prompts where prefix cache hit rate drops below ~20% — at which point RadixAttention adds overhead with no benefit.

Use SGLang when your workload is prefix-heavy — agent loops with stable system prompts, multi-turn chat with shared instruction prefixes, RAG applications where the retrieved context has shared structural framing. RadixAttention is the differentiator: it manages KV-cache as a radix tree with shared prefix nodes, giving 15–40% throughput win over vLLM's flat PagedAttention on prefix-hit workloads. SGLang's structured-generation (regex/JSON-schema/grammar-constrained decoding) is more mature than vLLM's — use it when output format guarantees matter. Switch to vLLM when prefix cache hit rate is below ~50% (diverse, one-shot prompts) or when you need the broader community and enterprise ecosystem. Use TensorRT-LLM for absolute lowest single-request latency on Hopper/Blackwell GPUs. Use Ollama for single-user desktop simplicity. SGLang's ecosystem is smaller than vLLM's but its structured generation and prefix tree are production-battle-tested on LMSYS Chatbot Arena serving.

Problem: CUDA out of memory during first request. Fix: RadixAttention allocates KV cache lazily on first request; reduce --mem-fraction-static from 0.9 default to 0.85 to leave KV-cache headroom. SGLang's KV cache grows dynamically as the prefix tree expands — budget ~15% more VRAM than equivalent vLLM deployment. Problem: Constrained generation returns empty or malformed output. Fix: The JSON schema or regex constraint conflicts with the model's tokenizer vocabulary boundaries. Test the constraint against the model's tokenizer using SGLang's constraint debugger: python -m sglang.check_grammar --model <model> --grammar <grammar>. Problem: RadixAttention tree memory grows unbounded over long server uptime. Fix: Set --max-total-tokens to cap total cached tokens. When exceeded, SGLang evicts by LRU leaf-first. Monitor tree size via the /server_info endpoint's tree_size field.