I compared Ollama, vLLM and LM Studio — only one of them survives a second user

at one person typing, all three are basically tied. add a second and the gap turns into a cliff

Aliteq

Lena Fischer · AI & Local Compute Editor

The short version

Single-user throughput is basically a wash — all three tools land in the same 130–180 tok/s range on the same hardware and model.

The short version

Under concurrent load, vLLM pulls dramatically ahead: roughly 793 tok/s vs. Ollama's ~41 tok/s at 128 simultaneous requests, per a widely cited Red Hat A100 benchmark.

The short version

Ollama defaults to OLLAMA_NUM_PARALLEL=1, meaning it queues requests one at a time unless you explicitly raise that limit.

The short version

LM Studio's 0.4.0 update added parallel requests with continuous batching — the first real narrowing of the gap for a GUI-first tool.

The short version

The tool matters less than VRAM. None of these fix a model that doesn't fit in memory.

my honest take

I'd default most solo local-AI setups to LM Studio now rather than Ollama, purely because the 0.4.0 batching update closes a real gap without giving up the easy GUI. That's a change from what I'd…

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I compared Ollama, vLLM and LM Studio — only one of them survives a second user

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