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摘要: vLLM vs Ollama 2026: 793 vs 41 TPS, 19x Gap [Tested]

Nadia Dubois

June 12, 2026

AI & Machine Learning

Nadia Dubois

June 12, 2026

21 min read

If you are choosing how to run open-weight large lan...

正文

vLLM vs Ollama 2026: 793 vs 41 TPS, 19x Gap [Tested]

Nadia Dubois

June 12, 2026

AI & Machine Learning

Nadia Dubois

June 12, 2026

21 min read

If you are choosing how to run open-weight large language models on your own hardware in 2026, the decision almost always comes down to

vLLM vs Ollama

. They sit at opposite ends of the same spectrum: Ollama is the friendliest way to pull a model and start chatting in one command, while vLLM is the high-throughput serving engine that powers production APIs at companies like Amazon and LinkedIn. Both are free and open source, both run on your own GPUs, and both have exploded in popularity – yet they are built for fundamentally different jobs.

This comparison cuts through the marketing. We pulled benchmarks from three independent 2025–2026 test labs (Red Hat, Spheron, and a widely-cited concurrency stress test), cross-checked GitHub data, and mapped real-world use cases. The headline result: in Red Hat’s August 2025 deep dive,

vLLM hit a peak of 793 tokens per second versus Ollama’s 41 TPS

on the same hardware – a roughly 19x throughput gap under load. But raw throughput is only half the story, and for a large share of developers Ollama is still the correct choice. Here is the full breakdown.

vLLM vs Ollama 2026: The Quick Verdict

Before the deep dive, here is the one-paragraph answer most people are searching for.

Choose Ollama if

you are a single developer, you want to run a model on your laptop or workstation, you value a one-command install, and you are serving one or a few users at a time.

Choose vLLM if

you are serving a model to many concurrent users, you have data-center GPUs (A100/H100-class), you need maximum tokens-per-dollar, and you are building a production API. Ollama optimizes for

; vLLM optimizes for

throughput at scale

. The two tools barely compete in practice – they win in different arenas, and many teams use both: Ollama for local prototyping, vLLM for production deployment.

来源: Brave/tech-insider.org

采集时间: 2026-06-12 20:17:11