I'm not sure what people are on in the comments. It doesn't beat the other models, but it sure competes despite its size. GLM 5.1 is an excellent model, but even at Q4 you're looking at ~400GB. Kimi K2.5 is really good too, and at Q4 quantization you're looking at almost ~600GB. This model? You can run it at Q4 with 70GB of VRAM. This is approaching consumer level territory (you can get a Mac Studio with 128GB of RAM…
Yeah, you can run it locally if you have enough VRAM, but the reports trickling in are saying about 3 tok/sec. This was on a Strix Halo box which definitely has the needed VRAM, but isn't going to have as high mem bandwidth as a GPU card, it's going to be similar on a Mac - that's the dilemma... the unified memory machines have the VRAM, but the bandwidth isn't great for running dense models. This size of a dense mod…
That's the edge of Apple Silicon for AI. When they scale up the chip they add more memory controllers which adds more channels and more bandwidth.
But yeah in the end it's still going to be only a handful of people that can run it.
What I meant is that I think researching and developing smaller more powerful model is more interesting than chasing the next 3T parameter model while burning through VC money and squeezing your customer base more and more aggressively.