Buying up old GPUs for AI might be the way to go for some smaller AI outfits Every time Nvidia drops a new flagship accelerator, the entire AI processing landscape reshapes practically overnight. Hyperscalers scramble for allocation and last generation’s hardware gets treated like it’s ancient history. But what doesn’t get anywhere near as much attention is the outgoing generation of accelerators — which …

New 102.4 tbps Silicon One promises efficiency gains for hyperscalers In sum – what we know: It’s easy to focus on the GPU arms race when talking about AI infrastructure, but the networking connecting all those accelerators is arguably almost as important. You can rack up as many GPUs as your budget allows — but if the data can’t shuttle between them …

How can quantization turn massive models into efficient tools without ruining their accuracy? Running large language models is expensive. The biggest ones pack hundreds of billions of parameters, each stored as a high-precision number that chews through memory, power, and premium hardware. But do we actually need all that precision? Increasingly, the answer is no. That realization has driven the …

Replacing copper with optical pipes could have a significant impact on the AI data bottleneck The semiconductor industry has been following the same steps for decades, revolving around shrinking the transistor and packing them more into a chip. It’s worked remarkably well. But there’s a problem emerging that transistor density simply can’t fix — getting data from point A to …