---
title: "Discussion on \"Ollama vs vLLM vs llama.cpp: Which Local LLM Engine?\" | Hashnode"
description: "Discussion on \"Ollama vs vLLM vs llama.cpp: Which Local LLM Engine?\". Originally published on DevToolHub.\n\nOllama vs vLLM vs llama.cpp comes down to one question: how many people will hit the model at once? For one developer on a laptop, pick Ollama or llama.cpp. For a"
url: "https://hashnode.com/posts/ollama-vs-vllm-vs-llama-cpp-which-local-llm-engine/6abe517a5fc863159262d968"
---

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# [Ollama vs vLLM vs llama.cpp: Which Local LLM Engine?](https://devtoolhub.hashnode.dev/ollama-vs-vllm-vs-llama-cpp-which-local-llm-engine?utm_source=hashnode&utm_medium=feed)

Originally published on DevToolHub. Ollama vs vLLM vs llama.cpp comes down to one question: how many people will hit the model at once? For one developer on a laptop, pick Ollama or llama.cpp. For a

Oct 1 · 9 min read · devtoolhub.hashnode.dev  
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-   [#ollama](https://hashnode.com/tag/ollama)
-   [#vllm](https://hashnode.com/tag/vllm)
-   [#llama-cpp](https://hashnode.com/tag/llama-cpp)
-   [#local-llm](https://hashnode.com/tag/local-llm)
-   [#ai](https://hashnode.com/tag/ai)

## Written by

[Amaresh Pelleti](https://hashnode.com/@amareswer)

@amareswer

  
[View profile](https://hashnode.com/@amareswer)

## Responses 2

Markdown supported · ⌘↵ to post

[Yunus Emre Vurgun](https://hashnode.com/@yunusemrevurgun)

[Oct 20](https://hashnode.com/posts/ollama-vs-vllm-vs-llama-cpp-which-local-llm-engine/6abe517a5fc863159262d968/comment/6ad7280893844e10940be453)

Good catch on the idle-machine rerun, that is the benchmarking habit most comparisons skip. One caveat on the Ollama default: the 4,096 context it picked is much smaller than llama.cpp's per-slot context, so long prompts get silently truncated unless you raise OLLAMA\_CONTEXT\_LENGTH or set num\_ctx. Worth noting if someone compares answer quality and not just tokens per second. Also, a first request after model load includes load time, so warming the model with a throwaway prompt keeps the single-request number clean.

[Amaresh Pelleti](https://hashnode.com/@amareswer)

[Oct 20](https://hashnode.com/posts/ollama-vs-vllm-vs-llama-cpp-which-local-llm-engine/6abe517a5fc863159262d968/comment/6ad7280893844e10940be453#reply-6ad7683fafdfef3accd87a10)

Good catches, both worth flagging. You're right that Ollama's context default scales with VRAM (4,096 under ~24GiB, which is exactly where most single-developer/laptop setups land — the use case this article points people toward for Ollama), and it truncates silently rather than erroring, so a benchmark can look clean on short prompts and quietly degrade on longer ones. OLLAMA\_CONTEXT\_LENGTH is the right lever for a server-wide default; num\_ctx overrides it per-request or in the Modelfile. The warm-up point is well taken too — cold model-load time folded into a single request is a classic way to make steady-state throughput look worse than it is. Might work both into a future revision of this piece.