> The idea is to have a chip with SRAM large enough to fit the entire model, so inference can happen entirely in-memory. [...] So how much internal memory does the latest Cerebras chip have? 44GB. This puts OpenAI in kind of an awkward position. 44GB is enough to fit a small model (~20B params at fp16, ~40B params at int8 quantization), but clearly not enough to fit GPT-5.3-Codex.
You don't really need to fit the entire model on a single chip. Just as with GPUs, you can shard the model across multiple chips. Of course when you have a long pipeline of chips that each token needs to pass through, that decreases the end-to-end tokens per second correspondingly.
So the size of GPT-5.3-Codex-Spark isn't limited by the memory of a single Cerebras chip, but the number of such chips that you can chain together and still hit the 1000 tokens per second target. Given that Cerebras offers models much larger than 40B at faster speeds https://www.cerebras.ai/pricing#exploration GPT-5.3-Codex-Spark is likely closer to GLM 4.7 in size. (≈355B total parameters, 32B active)
You don't really need to fit the entire model on a single chip. Just as with GPUs, you can shard the model across multiple chips. Of course when you have a long pipeline of chips that each token needs to pass through, that decreases the end-to-end tokens per second correspondingly.
So the size of GPT-5.3-Codex-Spark isn't limited by the memory of a single Cerebras chip, but the number of such chips that you can chain together and still hit the 1000 tokens per second target. Given that Cerebras offers models much larger than 40B at faster speeds https://www.cerebras.ai/pricing#exploration GPT-5.3-Codex-Spark is likely closer to GLM 4.7 in size. (≈355B total parameters, 32B active)