Comment by gcr

12 hours ago

DwarfStar4 is a small LLM inference runtime that can run DeepSeek 4. The blog post implies that it currently requires 96GB of VRAM.

For others who are lacking context :-)

Thanks. Outside of LLM circles, DS4 is usually a video game controller.

That's the flash version not the full model and only at Q2-3~ so while impressive it's still quite different from the full model.

  • Not really. I'm building now another fast C compiler with DeepSeek 4 Flash, and rarely have to step outside to use Pro or Sonnet, gpt or kimi-2.6. Flash is very capable of almost everything.

> The blog post implies that it currently requires 96GB of VRAM.

Has anyone tested what happens if you try and run this on lower-RAM Macs? It might work and just be a bit slower as it falls back on fetching model layers from storage.

  • It'd be way slower since you'd be doing that work every token

    • True (with 64GB RAM it'd have to fetch 20% of its active experts from disk already, about 650MB/tok at 2-bit quant - and that percentage rises quickly as you lower RAM further); my question is just a more practical one about whether it runs at all, how bad the slowdown is, and to what extent you might be able to get some of that decode throughput back by running multiple (slower) agent sessions in parallel under a single Dwarf Star 4 server.

>The blog post implies that it currently requires 96GB of VRAM.

From the Github page it seems it only supports Apple and DGX Spark. I have 128 GB of RAM and a 3090 but it probably won't work.