Capability · Framework — fine-tuning

SGLang

SGLang (Structured Generation Language) combines a programming model for controlled LLM output with a serving runtime whose RadixAttention caches across requests that share prefixes. For agentic or RAG workloads where the same system prompt is used thousands of times per second, SGLang often delivers 2-5x higher throughput than vLLM. It's become a go-to for high-scale production deployments of open-weight models.

Framework facts

Category: fine-tuning
Language: Python / C++ / CUDA
License: Apache 2.0
Repository: https://github.com/sgl-project/sglang

Install

pip install 'sglang[all]'

Quickstart

# Launch server
python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --port 30000

# Query (OpenAI-compatible)
from openai import OpenAI
client = OpenAI(base_url='http://localhost:30000/v1', api_key='EMPTY')
resp = client.chat.completions.create(model='default', messages=[{'role':'user','content':'hi'}])

Alternatives

vLLM — more mature, larger community
TensorRT-LLM — fastest on NVIDIA but harder setup
MLC-LLM — edge and browser targets
llama.cpp — CPU / small GPU runtime

Frequently asked questions

SGLang vs vLLM?

vLLM has broader model coverage and a larger ecosystem. SGLang is often faster on workloads with heavy prompt reuse (agents, RAG) thanks to RadixAttention. Benchmark on your workload — the margin varies widely.

Is the DSL required?

No — SGLang's serving runtime is useful even if you call it with OpenAI-compatible requests from any framework. The DSL (`sgl.function`, `sgl.gen`) is a nice-to-have for structured pipelines.

Sources

SGLang — docs — accessed 2026-04-20
SGLang on GitHub — accessed 2026-04-20