---
title: "Comment by Yunus Emre Vurgun on \"Ollama vs vLLM vs llama.cpp: Which Local LLM Engine?\" | Hashnode"
description: "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 muc"
url: "https://hashnode.com/posts/ollama-vs-vllm-vs-llama-cpp-which-local-llm-engine/6abe517a5fc863159262d968/comment/6ad7280893844e10940be453"
---

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

Comment [Ollama vs vLLM vs llama.cpp: Which Local LLM Engine?](https://hashnode.com/posts/ollama-vs-vllm-vs-llama-cpp-which-local-llm-engine/6abe517a5fc863159262d968)

[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.