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 …

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, …

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 …

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 …

Ultimately, a hybrid approach will probably win out The AI hardware conversation tends to fixate on the biggest, most powerful chips. But there’s a parallel hardware race that doesn’t generate …

FPGAs may not be as powerful as GPUs, but they’re a whole lot more flexible Field-programmable gate arrays, sit in an interesting middle ground in the AI hardware landscape, somewhere …

Connecting AI chips with interconnects is arguably just as important as the chips themselves Modern AI training has moved far beyond what any single GPU can accomplish. Training large language …

Memory shortage to continue due to massive demand, according to Synopsys CEO In sum – what we know: The memory chip shortage isn’t going anywhere soon. Synopsys CEO Sassine Ghazi …

Specs bumped to 2.3 kW and 22.2 TB/s bandwidth to cement leadership before launch later this year In sum – what we know: Nvidia’s grip on the AI accelerator market …

Will ASICs completely take over from the GPU workhorses? The world’s largest technology companies are doing something that looks, at first glance, like a bit of a contradiction. Amazon, Google, …