HPC News Bytes – 20260817

**Doug Black** (0:05)
Welcome to HPC News Bytes, a weekly show about important news in the world of supercomputing, AI, quantum computing, and other advanced technologies. Hi everyone, welcome to HPC News Bytes, I'm Doug Black and with me is Shahin Khan. The Riemann hypothesis is a math problem, in fact it's a standing challenge to mathematicians that dates to 1859 and offers a prize of $1 million to the math whiz who can solve it. Speaking for myself, when this problem is described as inscrutable, I absolutely believe that to be true. Anthropics' Claude AI did not solve the Riemann hypothesis, but it's been reported that Claude produced a substantial new result around it. The hypothesis says, roughly, that all of a particular class of zeros of the Riemann zeta function lie on one critical line. Mathematicians had previously proved that at least 41.6% do.
An unreleased Anthropic model increased that lower bound to 67.2%.
According to Anthropic, the model explored roughly 650 ideas using 60 sub-agents and about 31 million output tokens. Human mathematicians reviewed the resulting work and Claude also generated a formally verifiable proof. Importantly, that 67.2% does not mean that the Riemann hypothesis is two-thirds solved. Proving any fixed percentage below 100% is fundamentally different from proving that every qualifying zero lies on the line. Sadly, Anthropic itself says it does not expect this particular technique to prove the full conjecture, which means the Riemann hypothesis continues to maintain its inscrutability.

**Shahin Khan** (1:58)
A couple of months ago, we covered OpenAI's proof of a math problem that had been elusive for 80 years. This is fundamentally the same kind of a thing. AI is taking advantage of all the information that humans give it. It remembers it all and can look at many combinations of possibilities or iterate many more times than humans can. The impressive part, and we touched on it last time, is that it is able to make what I called a perceptive shot in the dark.
Now, it doesn't get more logical than math, but it's also impressive nevertheless that the model has enough logic to follow a good lead and synthesize the result. In this case, the model was given a very hard open-ended task, generated hundreds of approaches, rejected failures, coordinated many parallel agents, searched existing mathematical ideas, and ultimately produced something that experts considered new and valid.
That begins to look like actual computational research, and it accelerates the idea that such approaches can work for other scientific topics. We should expect that capable models will be given large inference budgets to see how much useful discovery they can produce.

**Doug Black** (3:14)
AMD's upcoming MI430X accelerator is taking a different design point from the industry's trend toward low-precision AI chips. AMD says the accelerator, scheduled for delivery next year, will deliver 288 teraflops of native floating point 64, 432 gigabytes of HP M4, and 23.3 terabytes per second of memory bandwidth. HPCWire reports that the FP64 figure is nearly four times that of AMD's MI355, and substantially above earlier expectations. Also striking is the comparison with NVIDIA.
Rubin provides about 33 teraflops of native FP64. Although NVIDIA is pursuing techniques such as Ozaki-based emulation to produce higher effective double-precision performance on suitable workloads, AMD is positioning the MI430X for traditional HPC modeling and simulation mixed with AI. DOE's forthcoming Discovery System at Oak Ridge and France's Alice Rococ exascale system are both slated to use MI430X accelerators.

**Shahin Khan** (4:32)
AI economics are pushing silicon towards low precision. GPUs started with 32 bits, worked very hard to get to 64 bits, and then they were discovered by AI and rapidly moved back down to 32, 16, and now eight bits and four bits. And there are algorithms that can use even fewer bits. Certain calculations can use algorithmic emulation, like the Ozaki model you mentioned, to use low precision hardware to produce high precision results and still come out ahead in terms of speed. But traditional scientific simulation is still important and has workloads that require native FP64 and do not map cleanly to emulation. The big question is exactly how substantial those 64-bit workloads are, and especially as AI libraries and approaches are used inside HPC applications. If it's a minor fraction, you can use much slower numerical emulation when algorithmic emulation doesn't work, and maybe that's okay. They would probably require code changes too.
But if it is really substantial, then native 64-bit hardware becomes necessary.
With MI430X, AMD is betting there will be enough demand for serious 64-bit hardware, while NVIDIA's approach is more aggressive with the use of low-precision hardware.

**Doug Black** (5:52)
The AI memory wall is under attack from several directions. Samsung unveiled ZHBM, which proposes stacking HBM vertically above the accelerator rather than placing it beside the processor.