LLM Inference Engines 2026: vLLM vs SGLang - Serve Models Faster and Cheaper (87k Stars)

Your self-hosted model answers in 8 seconds per request. Dedicated inference engines like vLLM cut that to 1-2 seconds and raise throughput dramatically.

## LLM Inference Engines: Why Ollama Is Not Enough for Production Ollama is perfect for a single-user local setup. But when you serve many users, you need an inference engine optimized for throughput, batching, and memory. vLLM (87,870 GitHub stars, Apache-2.0, from UC Berkeley) and SGLang are the two leading options in 2026. ## The Core Optimization: PagedAttention vLLM's breakthrough is PagedAttention - managing the KV cache (the memory holding context for every in-flight request) in fixed-size blocks like a virtual memory system. The result: near-zero memory waste, which means you can fit far more concurrent requests on the same GPU. A single A100-class card that served maybe 10 concurrent users with naive approaches can serve 50-100 with vLLM, at similar latency. ## vLLM: The Production Default vLLM is the most widely deployed open inference engine. You point it at a model (including GGUF-free HuggingFace weights), and it exposes an OpenAI-compatible API - your existing code works unchanged: ```bash vllm serve Qwen/Qwen2.5-7B-Instruct --tensor-parallel-size 1 ``` Features: continuous batching, prefix caching, quantization (AWQ, GPTQ), and speculative decoding for speed. It is the safe default for most teams. ## SGLang: The Speed Champion SGLang (from the LambdaLabs/Stanford community) adds radical optimizations on top of the same ideas - most notably RadixAttention, which caches shared prompt prefixes across requests, and a specialized interpreter for structured outputs. In community benchmarks through 2026 it frequently edges out vLLM on throughput for chat workloads with shared system prompts, at the cost of a younger ecosystem. ## How to Choose | Situation | Engine | |-----------|--------| | Default production serving | vLLM | | Maximum throughput, shared prefixes | SGLang | | Structured output heavy workloads | SGLang | | Best ecosystem/tooling | vLLM | Both are free (Apache-2.0). Both serve OpenAI-compatible APIs, so switching is a config change - run both behind a gateway (LiteLLM) and A/B them on your own workload. ## FAQ **Do I need a GPU?** Yes - these engines are GPU-first; CPU serving is possible but much slower. **vLLM vs Ollama?** Ollama is a convenience wrapper (great for local); vLLM is for production throughput. Ollama can even use a vLLM backend now. **Can it serve any model?** Any HuggingFace transformer model works, with quantization support for large ones. **How much VRAM do I need?** Rule of thumb: 2x the model size for a small batch; more for long contexts and high concurrency.