Not yet with MetalRT, right now we support models up to ~4B parameters (Qwen3 4B, Llama 3.2 3B, LFM2.5 1.2B). These are optimized for the voice pipeline use case where decode speed and latency matter more then model size.

Expanding to larger models (7B, 14B, 32B) on machines with more unified memory is on the roadmap. The Mac Studio with 192GB would be an interesting target, a 32B model at 4-bit would fit comfortably and MetalRT's architectural advantages (fused kernels, minimal dispatch overhead) should scale well.

What model / use case are you thinking about? That helps us prioritize.

I am running 80b Qwen coder next 4bit quant MLX version on a 96GB M3 MacBook and it responds quickly, almost immediately. I can fit the model + 128k context comfortably into the memory