SGLang

SGLang is an open-source LLM inference engine focused on high throughput for structured generation and complex agent workflows. It implements RadixAttention (a prefix-cache reuse mechanism across requests) and was designed to outperform vLLM on workloads with heavy prompt reuse, like RAG, function calling, and multi-turn chat. Operators use SGLang when their workload is dominated by shared prefixes — same system prompt across many users, retrieval contexts that repeat, agent traces that branch from common state.

Where vLLM treats each request as a fresh prefill + decode cycle, SGLang fingerprints the prefix tree across all in-flight requests and reuses the KV cache for any overlap. This matters most for serving agent workflows where the same system prompt + tool definitions show up in every call, and for RAG where the retrieved chunks vary but the surrounding template is fixed. SGLang also ships a frontend DSL for structured generation (regex-constrained output, JSON schema enforcement) that bakes the constraint into the sampler rather than retrying. The engine supports the same GPU runtimes as vLLM (CUDA on NVIDIA, ROCm on AMD) and is compatible with most HF-format weights, though some quantization paths lag behind llama.cpp.

An operator running a RAG system with a 2,000-token system prompt + a 500-token retrieved-chunk context, fielding 100 requests per minute, would see SGLang reuse the 2,000-token prefix across nearly every request — only the 500-token chunk and the user query change. The KV cache for the shared prefix gets computed once and referenced many times, which can multiply effective throughput several-fold over a setup that recomputes the prefill on every request. The exact uplift depends on prompt-reuse ratio and is workload-specific; benchmark on the real traffic shape before committing.

Installation is pip install sglang plus an HF token if the model is gated. A minimal server command looks like python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --port 30000 on an A100 or RTX 4090. The OpenAI-compatible endpoint at /v1/chat/completions slots into existing LangChain or LlamaIndex pipelines without code changes. Prefix-cache stats show up in the server logs as RadixAttention hit-rate — operators monitor that to confirm the workload is actually benefiting from the engine choice.