Sadly I couldn’t make it to the Cerebras event today b/c I was having fun with my friends. But this is what I gather… The new WSE-3T is on the same N5 node and same wafer size but doubled compute to 250 PF & doubled memory bandwidth to 43.2 PB/s which to me would mean a 2x clock increase which is minimum 2x the power. The prior WSE-3 consumed ~23 kW so let’s call it 40-55 kW per wafer. 3 wafers per rack or 130-180 kW for the entire system. Rubin NVL72 is 72 packages x 288 GB of HBM4 @ 22 TB/s for a total of 1.6 PB/s at roughly ~200 kW. So for CS-4 system we get ~0.9 PB/s per kW vs. ~8 TB/s per kW on Nvidia. So roughly 100x more bandwidth per kilowatt and in practice it’s probably more cause Nvidia relies more on interconnect than Cerebras. Sooooo prettay prettay good 👍 But only 132 GB of SRAM vs. ~21 TB of HBM memory + 75 TB of LPPDR on Nvidia it’s a lil scaaaary. I’m all for a decode optimized accelerator but when you can’t fit a Qwen 3.8 or DeepSeek V4 on it I don’t know how much of a value add it really is. If I still need to buy an Nvidia rack to go along with my Cerebras rack why would I really bother? Fast tokens spend faaaaast money 💰!!! Good if you’re rich but sadly most of us are working class token consumers who can’t afford a Bugatti model. That being said, the I/O board is a great addition (does it have FPGA???) and I don’t think the 7.2 TB/s is a big deal as latency is more important since this will only be used for inference. Also modular power solutions hmm .. 🤔 nice touch ! 😏 I see the vision. Giving Cerebras a B+ and I am excited to see what they do next, I can tell they are listening to some of the advice we gave them! Now let’s see if they are willing to diversify a bit so they can get those gross margins up. There’s still time to turn the boat 🛶 around!
