> ML_LIBRARY // SGLANG_v1.0
SGLang
LMSYS Org / UC Berkeley — Fast serving framework for large language models and complex multi-turn programs.
serving-inferencev0.3.1Apache-2.0qualified
Model Training
This library is a dedicated runtime engine for inference serving and does not train models.
Model Inference
Inference Accelerators:
CUDAROCM
Deployment Targets:server
Quantization:FP8, AWQ, GPTQ
What It Does
- +RadixAttention algorithm automatically caching and reusing shared prompt prefixes across requests
- +Fast constrained decoding for guaranteed JSON schema generation
- +Significant latency reductions for multi-turn agent conversations and few-shot RAG
What It Does Not Do
- -Train foundation models from scratch
- -Run on CPU-only machines effectively
- -Execute in browser environments
>Suitable Work Types
- Complex agentic workflows that repeatedly share large system prompts or tool schemas
- Few-shot RAG pipelines sharing long document contexts across queries
- High-throughput structured JSON extraction APIs
>Unsuitable Work Types
- Uncorrelated single-turn independent prompts with zero shared prefixes
- Edge microcontroller deployments
Data Residency Implications
GPU VRAM inside private server network.
Security Considerations
Radix cache keeps prompt memory across requests; ensure multi-tenant cache isolation if sharing across untrusted users.
Operational Profile & Known Limitations
Maturity:emerging
Learning Curve:moderate
Ops Complexity:moderate
Cost Tier:high-compute
> Known Limitations:
- Rapidly moving codebase with frequent architectural enhancements.
- Requires modern NVIDIA/ROCm GPU hardware.
Associated Incident Patterns (Incidentpedia)
Enforce safeguards and monitoring to guard against these documented real-world failure modes:
> Primary Evidence & Benchmark Citations
SGLang: Efficient Execution of Structured Language Model Programspaper • >=0.2.0, <=0.3.x
2026-09-25HIGH