MLX vs vLLM
MLX — apple's own array framework. The native path on Apple Silicon, with its own model format. 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
| MLX | vLLM | |
|---|---|---|
| Model formats | MLX (safetensors-based) | safetensors, AWQ, GPTQ, FP8, compressed-tensors |
| KV cache quantization | yes | yes |
| CPU offload | no | no |
| MoE expert offload | no | no |
| Multi-GPU | Not applicable — one chip, one unified memory pool. | Tensor parallelism — genuinely aggregates bandwidth, unlike a layer split. |
| Concurrency | Single-user focused. | This is the entire point. Hundreds of concurrent requests on one GPU. |
| Platforms | macOS | Linux |
Choose MLX if…
Apple Silicon, particularly at larger model sizes where the unified memory pool is the whole reason you bought the machine.
Its models are a separate format, so a GGUF you already downloaded will not work. Model availability is narrower than GGUF, though the popular families are all converted.
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.