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I was wondering the same thing. Training is much more memory-intensive so the usual low memory of consumer GPUs is a big issue. But with 128GB of unified memory
by bmenrigh 2y ago
I was wondering the same thing. Training is much more memory-intensive so the usual low memory of consumer GPUs is a big issue. But with 128GB of unified memory the Digits machine seems promising. I bet there are some other limitations that make training not viable on it.
- tpm 2y agoIt will only have 1/40 performance of BH200, so really not enough for training.
- jbarrow 2y agoPrimarily concerned about the memory bandwidth for training. Though I think I've been able to max out my M2 when using the MacBook's integrated memory with MLX, so maybe that won't be an issue.
- ryao 2y agoTraining is compute bound, not memory bandwidth bound. That is how Cerebras is able to do training with external DRAM that only has 150GB/sec memory bandwidth.
- jdietrich 2y agoThe architectures really aren't comparable. The Cerebras WSE has fairly low DRAM bandwidth, but it has a huge amount of on-die SRAM. https://www.hc34.hotchips.org/assets/program/conference/day2/Machine%20Learning/HC2022_Cerebras_Final_v02.pdf https://www.hc34.hotchips.org/assets/program/conference/day2...
- ryao 2y agoThey are training models that need terabytes of RAM with only 150GB/sec of memory bandwidth. That is compute bound. If you think it is memory bandwidth bound, please explain the algorithms and how they are memory bandwidth bound.