Every time Lisa Su, chief executive officer at AMD, announces a new Instinct GPU accelerator, the addressable market for AI acceleration in the datacenter seems to expand. …
With all of the hyperscalers and major cloud builders designing their own CPUs and AI accelerators, the heat is on those who sell compute engines to these companies. …
If high bandwidth memory was widely available and we had cheap and reliable fusion power, there never would have been a move to use GPU and other compute engines as vector and matrix math offload engines. …
Everyone is in a big hurry to get the latest and greatest GPU accelerators to build generative AI platforms. …
The world has gone nuts for generative AI, and it is going to get a whole lot crazier. …
For very sound technical and economic reasons, processors of all kinds have been overprovisioned on compute and underprovisioned on memory bandwidth – and sometimes memory capacity depending on the device and depending on the workload – for decades. …
It would be hard to find something that is growing faster than the Nvidia datacenter business, but there is one contender: OpenAI. …
In a world where GPUs have tens of billions of transistors and chip manufacturing techniques are costly and yields are particularly tough because of the largesse of these devices, every chip that comes of out the foundry is sacred. …
It is hard to remember sometimes way back when, in 2008, as Nvidia first took a stab at GPU compute in the datacenter with the original Tesla GPU accelerators and a very rudimentary CUDA programming environment for offloading parallel algorithms from CPUs to GPUs. …
A system is more than its central processor, and perhaps at no time in history has this ever been true than right now. …
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