Discover and read the best of Twitter Threads about #Cerebras
Most recents (2)
A short thread on accelerated computing. How $TSLA's #Dojo, $GOOG's #TPU #Cerebras's #WSE-2, #Graphcore's #IPU, and others compare to $NVDA 's #GPU. This is an extract of work we published a few weeks ago. 👇1/6
It is difficult to compare chips. Designing a chip is all about managing trade-offs between multiple dimensions. A chip has limited potential resources and the architect allocates them over these multiple dimensions. 2/6 
With this framework in mind, one sees alternatives to #GPU have fundamentally different architectures, favoring the flow of data across the chip vs. the flow of data between the memory and the chip. 3/6
I have tried to figure out why $TSLA designed Dojo instead of using #Cerebras last night. I almost lost sleep on it and I don't have an answer yet. Any input welcome. Thread delow for where I stand.👇
1 - a fan out wafer of Dojo and a wafer-scale chip of Cerebras have similar transistor density (~2.5 Trillions). Dojo claims 9 PFLOPS BF16, Cerebras 2.5 PFLOPS half precision, which is about equivalent, I think. (3+X ratio between the two is fair-same as FP32/64 ratio on Ampere)
2 - Dojo plans to scale out to ~120 Wafers to get to Exascale. Cerebras announced yesterday at Hotchips 192. Both configurations are similar, and deliver exascale compute. Both architecture should be able to go beyond that if needed as well.
