vLLM vs Ollama
vLLM — built for serving many concurrent requests. Much better throughput under load, and the wrong tool for one person chatting. Ollama — llama.cpp wrapped in a daemon and a model registry. Convenient, at the cost of some control and some lag. They share no model format, so switching means downloading again.
Side by side
| vLLM | Ollama | |
|---|---|---|
| Model formats | safetensors, AWQ, GPTQ, FP8, compressed-tensors | GGUF |
| KV cache quantization | yes | yes |
| CPU offload | no | yes |
| MoE expert offload | no | no |
| Multi-GPU | Tensor parallelism — genuinely aggregates bandwidth, unlike a layer split. | Inherits llama.cpp's layer split; little direct control. |
| Concurrency | This is the entire point. Hundreds of concurrent requests on one GPU. | Fine for a handful of concurrent requests, not for a production workload. |
| Platforms | Linux | Linux, macOS, Windows |
Choose vLLM if…
Serving an application or a team, where many requests arrive at once.
It does not read GGUF in any practical sense, wants the whole model resident, and has no useful CPU offload. For a single user on a consumer card it will usually be slower and pickier than llama.cpp.
Choose Ollama if…
Getting started, and any application that wants a local OpenAI-compatible endpoint without managing the engine.
Its short model names map to specific quantizations that are not obvious — a bare tag is usually a 4-bit build, not the model's best available. It also lags upstream, so a very new architecture may not load yet even though llama.cpp supports it.
Why there is no speed comparison here
We have not benchmarked these against each other, so we will not rank them on speed. Most of them wrap the same engine, which makes the differences that matter capability rather than throughput — and where a real speed gap exists it usually comes from configuration, such as how much of the model fits on the GPU and whether the cache is quantized, rather than from the runtime itself.