How vLLM differs from llama.cpp and Ollama, why it needs Linux and a server-grade GPU, what workloads it's built for, and when it's actually worth installing.

In engine comparisons, vLLM usually wins: higher throughput, better memory handling, more impressive benchmark numbers. After a comparison like that, it's tempting to want it for yourself.

The problem is those numbers come from a different job. vLLM is optimized for something you don't have on a home computer, and that's obvious from the install requirements alone.

What the requirements tell you

vLLM's official documentation answers "who is this for" better than any review could.

Operating system: Linux. Windows isn't natively supported — only through WSL or third-party forks. That alone rules out most home machines.

GPU: NVIDIA with compute capability 7.5 or higher — meaning RTX 20-series and up, or server cards like the A100, L4, H100. There's AMD ROCm support for MI200/MI300 and Radeon RX 7900/9000, Intel XPU, and a separate vLLM-Metal package for Apple Silicon. A CPU mode exists, but lives in nightly builds.

Python 3.10–3.13, a specific CUDA version, and for some backends, Python 3.12 only.

Compare that to llama.cpp, where you download an archive for your system and run the .exe. This isn't a difference in convenience — it's a difference in purpose: vLLM is a server component that engineers install into infrastructure.

What it's optimized for

vLLM's core idea is efficiently serving a lot of simultaneous requests. It smartly reuses memory across different users' contexts and batches requests together so the GPU never sits idle between them.

The payoff shows up when there really are a lot of requests: a hundred chat users, a document-processing queue, a production service. Then vLLM squeezes several times more total work out of one GPU than an engine built for a single user.

Now count your own requests. At a home computer, you send one request and wait for the answer. Then a second. There's no parallel load, nothing to optimize. A single answer's speed on vLLM won't be noticeably higher — it's bound by the same GPU and the same model either way.

Another difference: model format

llama.cpp and Ollama work with GGUF — a single file, compressed to size. There's a separate breakdown of the format and quantization.

vLLM targets models in their regular Hugging Face form, and that hits memory directly: where a GGUF version fits in 8 gigabytes of VRAM, the full-size version needs noticeably more. For a server with 80 gigabytes, that's irrelevant. For a home card, it's decisive.

When you actually need vLLM

Three situations:

You're standing up a service many people use at the same time.
You need to run a large batch of data through a model, and throughput matters more than response time.
You have a Linux server with a server-grade GPU that's sitting idle anyway.

In all three, vLLM is the right choice, and llama.cpp will lose out there.

When llama.cpp or Ollama is enough

If there's one user and an ordinary machine — everywhere else. The difference between an engine and a wrapper is covered separately, but for a home scenario, both options give you the same response time as vLLM, with incomparably less installation hassle.

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A simple check for whether you need vLLM: look at how many requests hit your model at once. If the answer is "one, mine," there's no payoff — all of vLLM's optimization is about parallelism.

What's actually worth optimizing instead of the engine

Worth stepping back here. "Which engine is faster" seems like the main question until you actually count where the time goes.

The model answers in seconds. Then you read the answer, copy a piece into a file, edit it by hand, come back with a follow-up, copy again. That loop takes an order of magnitude longer than generation, and no engine shortens it — it's simply not what engines are about.

Doka shortens exactly that loop. The agent opens files itself, makes edits in place, runs commands, and checks the result, while a local model installs from the interface with no backend or CUDA version to pick. The engine underneath is the most ordinary one — because for a single person, there's no speed difference between engines, but the difference between "got some text" and "the work is done" is enormous.

So if you're choosing between vLLM and llama.cpp for a home machine, the honest answer is: take llama.cpp and don't think about it further. Spend the evening you saved on trying an agent on your own real task instead.

vLLM, Ollama, and llama.cpp: Why the Fastest Engine Isn't What You Need at Home