alt Hacker News> 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.
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Is there reason to expect the consumer hardware markets to recover any time soon?
Is there reason to expect they’ll ever recover without an AI bust that takes down the U.S. economy?
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.
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.