Currently the setup is paged view in RAM shuttled to HBDRAM (VRAM) on the GPU, which in turn has to get materialized piece by piece onto cache SRAM on the GPU.
Cerebras tries to get around this by keeping everything on cache SRAM as much as possible, which it burns directly to the chip wafer itself and physically places that SRAM directly next to the tiny compute unit that does the actual math.
An ideal setup (not sure how easy this is to achieve in practice), is the burn the weights of the model directly to the chip as a sort of ROM, the actual math operations as actual digital circuits, and have SRAM, or even something akin to naked registers to directly compute off inference batch data. Cuts out 2-3 layers of abstraction and indirection.
You don't need memory because the output of the previous layer flows directly into the input gates of the next. So no I/O to/from HBM or similar
You still need some memory for the context, in flight answers, ... but not for the model weights and for the output of the intermediate layers. I found taalas demo here: https://chatjimmy.ai/
Currently the setup is paged view in RAM shuttled to HBDRAM (VRAM) on the GPU, which in turn has to get materialized piece by piece onto cache SRAM on the GPU.
Cerebras tries to get around this by keeping everything on cache SRAM as much as possible, which it burns directly to the chip wafer itself and physically places that SRAM directly next to the tiny compute unit that does the actual math.
An ideal setup (not sure how easy this is to achieve in practice), is the burn the weights of the model directly to the chip as a sort of ROM, the actual math operations as actual digital circuits, and have SRAM, or even something akin to naked registers to directly compute off inference batch data. Cuts out 2-3 layers of abstraction and indirection.
I assume ROM would be even more expensive than SRAM?
You don't need memory because the output of the previous layer flows directly into the input gates of the next. So no I/O to/from HBM or similar
You still need some memory for the context, in flight answers, ... but not for the model weights and for the output of the intermediate layers. I found taalas demo here: https://chatjimmy.ai/