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What is SGLang and Why Does It Matter?
Imagine you’re working with big language models (LLMs), but everything feels slow. The responses take forever, and the costs keep going up. What if there was a way to make things faster, cheaper, and more efficient? That’s where SGLang comes in.
SGLang is an open-source LLM inference engine that can process 2–5 times more requests than other solutions. This means less waiting, lower bills, and more power. But how does it do that? Let’s break it down.
🚀 Why SGLang is a Big Deal
🔍 RadixAttention & Smart Prompt Handling → 5x Faster for Chat & RAG
Most LLM engines recompute the same prompt again and again for different requests. SGLang is smarter — it reuses the shared parts of prompts, making chatbots and retrieval-augmented generation (RAG) systems up to 5 times faster.
⚡ 3.1x Speed Boost for Structured Output (JSON/XML)
When generating structured data, most models go step by step, even when some parts never change. SGLang skips predictable elements, making JSON and XML outputs much quicker and smoother.
🖥 No More CPU Bottlenecks → Optimized Scheduling
A common issue with LLMs is that the CPU slows things down, even when the GPU is free. SGLang optimizes CPU scheduling, removing these delays and keeping things running at top speed.
🔥 Record-Breaking Performance
- 5,000 tokens per second on Llama3–8B (A100 GPU)
- 10,000 tokens per second on Llama3–70B (8x H100 cluster)
🌍 Used by Big Tech Companies
- Meituan, ByteDance, and xAI rely on SGLang.
- ByteDance runs 70% of its NLP tasks using SGLang, processing 5 petabytes of data daily.
- xAI cut the cost of running Grok by 37% using KV cache reuse and optimized scheduling, handling 23M+ chats per day.
🔗 Open-Source & Easy to Use
- Licensed under Apache 2.0 ✅
- Works with OpenAI API ✅
- Has a Python API ✅
💾 Supports Multiple Hardware & Models
- Works on NVIDIA & AMD GPUs 🖥
