Those are scribe lines where you usually would cut out chips which is why it resembles multiple chips. However, they work with TSMC to etch across them.
feels very low compared to claude/gpt for me
I don't think they rely on SRAM very much for training. https://cerebras.ai/blog/the-complete-guide-to-scale-out-on-... outlines the memory architecture but it seems like they are able to keep most of the storage off…
Seems like they support training on a bunch of industry standard models. I think most of the customers in the training space tend to be for fine tuning right? The P and T in GPT stand for pre-trained - then you tune for…
afaik they have the current SOTA language models for arabic
MLPerf brings in exactly zero revenue. If they have sold every chip they can make for the next 2+ years, why would they be diverting resources to MLPerf benchmarking? Artificial analysis does good API provider inference…
Batched inference will increase your overall throughput, but each user will still be seeing the original throughput number. It's not necessarily a memory vs compute issue in the same way training is. It's more a…
That was more of a WSE-1 problem maybe? They switched to a new compute paradigm (details on their site if you look up "weight streaming") where they basically store the activation on the wafer instead of the whole…
Those are scribe lines where you usually would cut out chips which is why it resembles multiple chips. However, they work with TSMC to etch across them.
feels very low compared to claude/gpt for me
I don't think they rely on SRAM very much for training. https://cerebras.ai/blog/the-complete-guide-to-scale-out-on-... outlines the memory architecture but it seems like they are able to keep most of the storage off…
Seems like they support training on a bunch of industry standard models. I think most of the customers in the training space tend to be for fine tuning right? The P and T in GPT stand for pre-trained - then you tune for…
afaik they have the current SOTA language models for arabic
MLPerf brings in exactly zero revenue. If they have sold every chip they can make for the next 2+ years, why would they be diverting resources to MLPerf benchmarking? Artificial analysis does good API provider inference…
Batched inference will increase your overall throughput, but each user will still be seeing the original throughput number. It's not necessarily a memory vs compute issue in the same way training is. It's more a…
That was more of a WSE-1 problem maybe? They switched to a new compute paradigm (details on their site if you look up "weight streaming") where they basically store the activation on the wafer instead of the whole…