LM Studio vs vLLM
LM Studio — a desktop application over llama.cpp and MLX, with model discovery and hardware-aware suggestions built in. vLLM — built for serving many concurrent requests. Much better throughput under load, and the wrong tool for one person chatting. They share no model format, so switching means downloading again.
Side by side
| LM Studio | vLLM | |
|---|---|---|
| Model formats | GGUF, MLX | safetensors, AWQ, GPTQ, FP8, compressed-tensors |
| KV cache quantization | yes | yes |
| CPU offload | yes | no |
| MoE expert offload | no | no |
| Multi-GPU | Basic; exposes fewer controls than the engine underneath. | Tensor parallelism — genuinely aggregates bandwidth, unlike a layer split. |
| Concurrency | Local server for personal use. | This is the entire point. Hundreds of concurrent requests on one GPU. |
| Platforms | macOS, Windows, Linux | Linux |
Choose LM Studio if…
People who would rather not use a terminal, and Apple Silicon users who want MLX without setting it up themselves.
Closed source, and its convenience layer hides some of the memory controls that matter when a model is close to not fitting.
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.
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.