Distributing LLM Inference in DwarfStar

High end NVIDIA cards, and the server and power needed to run them, cost a lot of money, especially if you plan to reach enough VRAM to run massive models. The alternative, so far, has been Apple hardware, or the DGX Spark that, even if severely limited because of memory bandwidth, still allows to run LLMs prompt processing (prefill) fast enough. The Mac Studio provided up to 512GB unified memory, a solution with modest memory bandwidth (but much better than the Spark) and compute at a price that was, after all, given the current situation, relatively fair.

For instance, with DwarfStar the Mac Studio M3 Ultra 512GB can run DeepSeek v4 PRO at 150 t/s prefill and ~10-13 t/s decoding, not great but at a level that is usable for certain use cases. Even 2-bit quantized, DeepSeek v4 PRO resists very well, like Flash at the same quantization (today I made PRO write a C compiler, I'll publish the video soon). I would not consider a trivial fact to run a frontier model at home, with a ~12k total spending.

One could expect this to get better and better, but the situation at the horizon appears cloudy. There is almost zero hope that NVIDIA setups will get less expensive, and even a small company can’t afford to easily purchase and handle a small data center for local inference. At the same time the RAM shortage is making it not exactly likely that we will see a Mac Studio with an M5 Ultra, maybe 1.2T/s memory bandwidth and more compute (the M5 Max is already faster, compute wise, and has the Neural Accelerators inside each GPU core that help with certain models).

So the current situation for local inference is that the best machine is probably a laptop. The M5 Max 128GB can run DeepSeek v4 Flash and Mimo V2.5, 2-bit quantized, at very decent prefill and decoding speeds. We are talking of ~500 t/s prefill and ~35-40t/s decoding speed, with a performance slope as the context size increases which is very acceptable. At the cost of 6-7k depending on the configuration, this is…