With Blackwell GPUs, AI Gets Cheaper And Easier, Competing With Nvidia Gets Harder
If you want to take on Nvidia on its home turf of AI processing, then you had better bring more than your A game. …
If you want to take on Nvidia on its home turf of AI processing, then you had better bring more than your A game. …
It is a pity that we can’t make silicon wafers any larger than 300 millimeters in diameter. …
If you handle hundreds of trillions of AI model executions per day, and are going to change that by one or two orders of magnitude as GenAI goes mainstream, you are going to need GPUs. …
Ultimately, every problem in the constantly evolving IT software stack becomes a database problem, which is why there are 418 different databases and datastores in the DB Engines rankings and there are really only a handful of commercially viable operating systems. …
With the reticle limit for chip manufacturing pretty much set in stone (pun intended) at 26 millimeters by 33 millimeters down to 2 nanometer transistor sizes with extreme ultraviolet lithography techniques and being cut in half to 26 millimeters by 16.5 millimeters for the High-NA extreme ultraviolet lithography needed to push below 2 nanometer transistor sizes, chiplets are inevitable and monolithic dies are absolutely going to become a thing of the past. …
Things would go a whole lot better for server designs if we had a two year or better still a four year moratorium on adding faster compute engines to machines. …
Note: This story augments and corrects information that originally appeared in Half Eos’d: Even Nvidia Can’t Get Enough H100s For Its Supercomputers, which was published on February 15. …
By the time that the founders of Achronix, who were all techies from Cornell University, decided to found their own FPGA company twenty years ago, FPGAs had already been in the field for twenty years and the market was dominated by Xilinx (now part of AMD) and Altera (still part of Intel until it gets spun out sometime in the future). …
It is a strange time in the generative AI revolution, with things changing on so many vectors so quickly it is hard to figure out what all of this hardware and software and people-hours costs and what it might be worth when it comes to transforming, well, just about everything. …
It is beginning to look like the Dell Technologies and Hewlett Packard Enterprose, the world’s two biggest original equipment manufacturers, are finally going to start benefitting from the generative AI wave, mainly because they are finally getting enough allocations of GPUs from Nvidia and AMD that they can start addressing the needs of customers who don’t happen to be among the hyperscalers and largest cloud builders. …
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