Quantum computing is finally heating up. There is a heady mix of high-profile and highly resourced big tech players like Google, Microsoft, Amazon Web Services, and Nvidia either building QPUs, simulating them, or integrating them with classical supercomputers in addition to well-funded younger companies and startups, such as QuEra, IonQ, Quantum Computing, Quantinuum, D-Wave, and Alice & Bob. …
Over the next few years, Big Blue may not build the biggest AI business in the world by any stretch of the imagination, but we do have confidence that it will build a collection of AI products and services that are among the most profitable. …
Big Blue may have missed the boat on being one of the big AI model builders, but its IBM Research division has built its own enterprise-grade family of models and its server and research divisions have plenty of experience building accelerators and supercomputers. …
It is a now well-known fact in the datacenters of the world, which are trying to cram ten pounds of power usage into a five pound bit barn bag, that liquid cooling is an absolute necessity for the density of high performance computing systems to be increased to drive down latency between components and therefore drive up performance. …
As we talked about a decade ago in the wake of launching The Next Platform, quantum computers – at least the fault tolerant ones being built by IBM, Google, Rigetti, and a few others – need a massive amount of traditional Von Neumann compute to help maintain their state, assist with qubit error correction, and assist with their computations. …
D-Wave executives stirred up some controversy earlier this year when they claimed a smaller version of its Advantage 2 annealing quantum system, armed with 1,200 qubits, had reached “quantum supremacy,” – or “quantum advantage” – that significant but ill-defined time when a quantum system is able to solve a problem in much less time, at a lower cost, or more efficiently than the most powerful classical supercomputer. …
While the hyperscalers and clouds and their AI model builder customers are setting the pace in compute, networking, and storage during the GenAI revolution, that does not mean that they will necessarily provide the only systems that will be used by the largest enterprises in the world. …
If you need a big, badass box that can support tens of terabytes of memory, dozens of PCI-Express peripheral slots, thousands of directly attached storage devices, all feeding into hundreds of cores that can span that memory footprint with lots of bandwidth, you do not have a lot of options. …
We don’t normally spend a lot of time writing about IBM mainframes, but these big iron systems drive a lot of transactions in the world – transactions flush with demographics and context that will feed into AI models – and will be doing native and integrated AI processing for the applications that push those applications. …
Updated with just-announced Intel roadmap changes.
It is often said that companies – particularly large companies with enormous IT budgets – do not buy products, they buy roadmaps. …
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