Ollama vs vLLM vs SGLang: A Beginner's Guide to Serving Open LLMs

Pulling an open-weight model onto your machine is the easy part. Deciding how to run it—especially once more than one person wants answers at the same time—is where most beginners get stuck.

Three names show up in almost every self-hosting conversation: Ollama, vLLM, and SGLang. They are not three flavors of the same product. Each solves a different serving problem, and picking the wrong one usually feels like "the GPU is fine but the API is slow."

This guide walks through what each engine is for, how they differ under the hood in plain language, and a simple way to choose without memorizing every benchmark chart on the internet.

What an Inference Engine Actually Does

A model file is weights. An inference engine is the runtime that turns those weights into tokens under real traffic: it loads the model, manages GPU memory for attention state (the KV cache), decides which requests run together, and streams tokens back.

Running a chat in a notebook is one request. Serving a product is many overlapping requests that start and finish at different times. Engines differ mainly in how smart they are about batching those requests and reusing work they have already done.

Three Engines, Three Jobs

Ollama: Ship a Local Demo First

Ollama optimizes for setup speed and developer experience. You pull a model, it comes up with an OpenAI-compatible endpoint, and you can iterate on prompts without fighting CUDA builds. Under the hood it leans on llama.cpp-style GGUF execution and a more conservative request path than production GPU servers.

That design is intentional. Ollama is excellent when you are the only concurrent user—or when a small team is prototyping. It is a poor fit when you need stable latency for dozens or hundreds of overlapping requests. If your "API" is really a shared laptop process, you will feel queueing and uneven speed long before the model quality is the problem.

vLLM: Keep the GPU Fed

vLLM became the production default for a reason. Continuous batching lets finished requests leave the batch and new ones join without waiting for everyone else to finish. PagedAttention manages the KV cache in fixed-size blocks—more like an OS paging memory than reserving one giant contiguous slab per request—so fragmentation wastes less VRAM.

In practice, that means one GPU can serve far more concurrent users than a naive loop. If your traffic is many independent prompts (different users, little shared context), vLLM is usually the safest first production choice. It also tracks a wide model zoo quickly, which matters when your roadmap changes every quarter.

SGLang: Reuse What You Already Computed

SGLang sits in the same high-throughput family as vLLM, but its signature idea is RadixAttention: keep computed KV prefixes in a radix tree and reuse them when new requests share a prompt head. A long system prompt, a shared RAG document, or a growing multi-turn history does not need to be recomputed from scratch every time.

The scheduler is prefix-aware as well—it prefers work that will hit the cache. That pays off for agent tool loops, chatbots with stable preambles, and constrained decoding (JSON schemas, regex) where the same structure shows up repeatedly.

If almost every request is unique end-to-end, the advantage shrinks and you should pick based on ops familiarity and model support. If more than about half your tokens sit in shared prefixes, SGLang is worth a serious bake-off.

Side-by-Side Comparison

How to Choose in Practice

Public throughput numbers move every release cycle. Treat them as direction, not destiny: measure time-to-first-token and tokens per second on your prompts, with prefix caching enabled where available, before you standardize.

Mistakes Beginners Make

Quick Recap

Ollama gets you from zero to a local chat API with almost no friction. vLLM squeezes more concurrent users out of the same GPU for general traffic. SGLang pays off when shared prefixes dominate— agents, RAG, and multi-turn systems.

Start where your workload lives today, not where a chart says the "winner" is. Move engines when concurrency or prefix reuse becomes the bottleneck—not when a blog post tells you to.