With Apple devices you get very fast predictions once it gets going but it is inferior to nvidia precisely during prefetch (processing prompt/context) before it really gets going.

For our code assistant use cases the local inference on Macs will tend to favor workflows where there is a lot of generation and little reading and this is the opposite of how many of use use Claude Code.

Source: I started getting Mac Studios with max ram as soon as the first llama model was released.

> With Apple devices you get very fast predictions once it gets going but it is inferior to nvidia precisely during prefetch (processing prompt/context) before it really gets going

I have a Mac and an nVidia build and I’m not disagreeing

But nobody is building a useful nVidia LLM box for the price of a $500 Mac Mini

You’re also not getting as much RAM as a Mac Studio unless you’re stacking multiple $8,000 nVidia RTX 6000s.

There is always something faster in LLM hardware. Apple is popular for the price points of average consumers.

This. It's awful to wait 15 minutes for M3 Ultra to start generating tokens when your coding agent has 100k+ tokens in its context. This can be partially offset by adding DGX Spark to accelerate this phase. M5 Ultra should be like DGX Spark for prefill and M3 Ultra for token generation but who know when it will pop up and for how much? And it still will be at around 3080 GPU levels just with 512GB RAM.

All Apple devices have a NPU which is potentially able to save power for compute bound operations like prefill (at least if you're ok with FP16 FMA/INT8 MADD arithmetic). It's just a matter of hooking up support to the main local AI frameworks. This is not a speedup per se but gives you more headroom wrt. power and thermals for everything else, so should yield higher performance overall.

Vllm-mlx with prefix caching helps with this.