In their announcing page, the section "How can we fit so much more FLOPS on our chip than GPUs?" tells some details. It's said "only 3.3% of the transistors on an H100 GPU are used for matrix multiplication". They trade off programmbility with computation density. And from the "Isn’t inference bottlenecked on memory bandwidth, not compute?" section, I guess they use similar tricks like Groq. Looking forward to more architecture details and comparation with Groq.
LLMs that are attached to normal CPUs need lots of fast memory because they are doing very large matrix operations with very few arithmetic units which implies a lot of data motion. Changing that architecture might save on the need to move so much data, but it isn't at all clear what these people are proposing.
It also isn't at all obvious why their stuff would be any better than an ordinary vectorized arithmetic unit (often provocatively called a "tensor" chip).