OpenAI is launching GPT-5.6 Sol on Cerebras at up to 750 tokens per second in July.
Bleys Goodson estimates Sol may be served across 70-100 Cerebras wafers, with roughly one model layer per wafer: around 3T total parameters, 150B active, 70 layers.
It suggests OpenAI did not just place a frontier model onto another inference provider. It likely designed the model around the hardware! Cerebras would move from “fast inference for smaller models” into something much more strategic: serving frontier intelligence at extreme speed.
Game changer, big win for OpenAI. It cannot be emphasized enough how important this is.
It is a 2 to 4T param model. They are serving it across 70-100 wafers. To get healthy serving characteristics, they are essentially putting at most one layer per wafer, and the model is in the ballpark of 70-90 layers.
There's a couple of different ways this could be served and model sizes implied by that. One is if they keep the heavy KV caches they've used before. Another is if they go with lighter KV cache designs more akin to DeepSeekV4 or Hybrid SSM models.
The fact that they've partnered with Cerebras and designed with the hardware in mind means they're much more likely to have gone the second route. That SRAM bandwidth is too precious for a heavy KV cache. As such, something like the below is the center of probability mass: 3T total, 150B active, 70 layers.
