MiniMax M2.5 tops 62 tok/s on M5 Max

This reads less like hype and more like a useful proof point: a 230B-class open-weights model is starting to feel practical on serious consumer hardware if you’re willing to quantize aggressively and accept some tradeoffs.

• –62 tok/s on a laptop-class machine is genuinely impressive, but the `UD-Q3_K_XL` quantization is doing a lot of the heavy lifting here.
• –The OpenAI-compatible server shape matters as much as the raw speed, because it makes the model much easier to drop into agent and app workflows.
• –MiniMax’s own docs position M2.5 for local/private deployment, so this post is a real-world validation of that story rather than a one-off stunt.
• –The public API angle turns a personal workstation into a shareable inference endpoint, but production users would still want to validate latency consistency, memory headroom, and access controls.