> This is not a local model for any reasonable definition of local
That's true for now. I am hopeful that once the hardware markets have recovered from OpenAI's sabotage, we will see more hardware dedicated to local inference that can handle these big models.
Also, I'm thinking about the unique MoE routing that Apple is using with their new Apple Foundation Model. The model is trained and architected so that experts are not swapped for every token, but only occasionally. This suggests that e.g., a 744B parameter model in the future could have experts offloaded to SSD and still run with the effective computing requirements of a 40B model.
Reading weights out of memory is the definition of a large linear read. I'm a bit mystified someone hasn't put an embarrassingly parallel flash storage controller next to some tensor processors on a PCIe card. It could have 4Tb of flash hanging off enough channels to saturate SRAM skipping DRAM entirely, and could even offload prompt processing to a GPU in the same workstation so long as it got reasonable tokens/s in inference. I'd buy one tomorrow.
For the last year, there has been development work at several companies for products including HBF (high-bandwidth flash memory) as a supplement to HBM, in order to enable running inference for big LLMs at a reasonable cost, e.g. on one GPU-like card.
HBF was initially announced by SanDisk, early in 2025, then early this year Hynix has announced that they have joined SanDisk in producing HBF, and that the common specification will be standardized under the Open Compute Project.
With HBF, it would be easy to make a GPU card with 4 TB of HBF, which could run the biggest existing open weights LLMs in their native unquantized form.
Normally, experts are picked for every layer not just every token. But there are plausible ways of getting around that bottleneck while streaming if you can batch many inferences together. Still, the Apple approach of swapping the experts only rarely is interesting, though it likely degrades the model a lot.
That's true for now. I am hopeful that once the hardware markets have recovered from OpenAI's sabotage, we will see more hardware dedicated to local inference that can handle these big models.
Also, I'm thinking about the unique MoE routing that Apple is using with their new Apple Foundation Model. The model is trained and architected so that experts are not swapped for every token, but only occasionally. This suggests that e.g., a 744B parameter model in the future could have experts offloaded to SSD and still run with the effective computing requirements of a 40B model.