4 ms·
It is my understanding that just baking the model itself into silicon only gives moderate gains because memory bandwidth remains a bottleneck.
by adrianN 2mo ago
It is my understanding that just baking the model itself into silicon only gives moderate gains because memory bandwidth remains a bottleneck.
- chorizo 2mo agoThe big benefit is ROM cells require fewer components than DRAM. So the chips would be tiny, dense, cheap and consume far less power.
- klodolph 2mo agoI thought DRAM was pretty dense already. Is mask ROM that much denser?
- chorizo 2mo agoYes, each rom bit can be a transistor or even a diode with a decoder circuit. Simplest Dram cell is capacitor+transistor - and you need a clock, refresh circuit etc. Someday, I imagine model weights could even be encoded as analog resistors (memristors or similar) for even greater density
- ReactiveJelly 2mo agoHm. I wonder how many relays I'd need to make a physical MNIST classifier. That'd be dope
- Tuna-Fish 2mo agoIt's not even just that. If you just built the rom chips separately and swapped them for the RAM of a normal accelerator, it would not help at all. The trick is that every compute element in their system has it's own small pool of ROM, instead of putting all the ram behind a common pipe. ROM is just used because it's the densest kind of memory that can be fabricated on the same process as their logic.
- lelanthran 2mo agoDid you try using the the talaas chat? Something stupid like 18k tokens/second. Think it's called Askjimmy or similar.
- jgilias 2mo agoOh boy, thanks for sharing this, truly mind blowing. It was chatjimmy.ai
- TeMPOraL 2mo agoWhat is the model they're using there though? Interrogated, it claims it's a BERT variant and has capabilities around GPT-3 and below GPT-4. (Not that I believe it, it writes too well for GPT-3.) Hosted frontier models from two years ago would be much faster today, too.
- egorfine 2mo agoThey run Llama 3.1 8B.