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.

💡 What You Will Learn

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.

📜 Table of Contents

• LLM Inference Engines: Why Ollama Is Not Enough for Production
• The Core Optimization: PagedAttention
• vLLM: The Production Default
• SGLang: The Speed Champion
• How to Choose
• FAQ

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:

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

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.

❓ 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.