Waferscale compute engine and AI system maker Cerebras Systems has filed with the US Securities and Exchange Commission to sell a chunk of itself to the public, giving we outsiders a view of the past two and a half years of its internal financials. …
A scant three months ago, when Meta Platforms released the Llama 3 AI model in 8B and 70B versions, which correspond to the billions of parameters they can span, we asked the question we ask of every open source tool or platform since the dawn of Linux: Who’s going to profit from it and how are they going to do it? …
Updated: Here is something we don’t see much anymore when it comes to AI systems: list prices for the accelerators and the base motherboards that glue a bunch of them together into a shared compute complex. …
The only way to accurately predict the future is to live it, but just the same, prognostication is one of the things that we humans love to do. …
Amazon Web Services may not be the first of the hyperscalers and cloud builders to create its own custom compute engines, but it has been hot on the heels of Google, which started using its homegrown TPU accelerators for AI workloads in 2015. …
When hyperscalers and cloud builders think about their infrastructure, they talk about megawatts and they think about the mix of serving and storage and the total capacity that is delivered in a megawatt of power. …
After its acquisitions of ATI in 2006 and the maturation of its discrete GPUs with the Instinct line from the past few years and the acquisitions of Xilinx and Pensando here in 2022, AMD is not just a second source of X86 processors. …
Nvidia is not the only company that has created specialized compute units that are good at the matrix math and tensor processing that underpins AI training and that can be repurposed to run AI inference. …
Industry benchmarks are important because, no matter that comparisons are odious, IT organizations nonetheless have to make them to plot out the architectures of their future systems. …
As Google’s batch sizes for AI training continue to skyrocket, with some batch sizes ranging from over 100k to one million, the company’s research arm is looking at ways to improve everything from efficiency, scalability, and even privacy for those whose data is used in large-scale training runs. …
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