Mistral Medium 3.5

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 model is only going to be runnable (usefully) by very few people who have multiple GPU cards with enough memory to add up to about 70GB.

It’s 128b dense model. Good luck getting more than 3t/s out of a mac. It doesn’t matter if it fits or not.

I'm using mistral-medium-2508 for some text transformation operations. It's giving me better results than mistral-large for my use cases. Looking forward to testing this new model, although I'm not sure if it's really meant at replacing the previous medium model since it's a lot more expensive and presented more as a coding / agentic model (mistral-medium-2508 was priced $0.4/$2 per 1M tokens, mistral-medium-3.5 is $1,5/$7.5).

> being able to run a model and being able to run a model fast are two very different thresholds Specifically speaking, on my Strix Halo machine with (theoretical) memory bandwidth of 256 GB/s, a 70 GB model can't generate faster than 256/70= 3.65 t/s. The logic here is that a dense model must do a full read of the weights for each token. So even if the GPU can keep up, the memory bandwidth is limiting. A Mac M5 Pro is faster with a bandwidth of 307 GB/s, but that's only a little faster. This thing is going to be slow on consumer hardware. Maybe that is useful for someone, but I probably prefer a faster model in most cases even if the model isn't quite as smart. Qwen3.6 35B-A3B generates about 50 t/s on my machine, so it can make mistakes, be corrected, and try again in the same time that this model would still be thinking about its first response.