3 ms·

I agree; I don't think there's any reason to assume Jevons paradox won't apply. > If we have to switch to something like burning the model weights into silicon

• kurthr 2mo ago
  What's really interesting is that if you scale it to higher densities (eg 3nm and stacked die) with ComputeInMemory for fp8 you can reasonably start to fit 30B-70B models. With MoE and multiple stacked die, just like HBM, you could fit an open weight near frontier 1T model (like GLM5.2) at similarly much lower power <10kW and high token rates >2ktps. For running a bunch of agents where fill rate and speed/latency are important it may not matter that you're 6-18mo behind on weights. The process for the chip design could be largely automated, and new silicon pumped out as new weights are available (with a 3-6mo delay).