HPC News Bytes – 20261005

SPEAKER_1 (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.

Doug Black (0:14)

Hi, everyone. Welcome to HPC News Bytes. I'm Doug Black, and with me is Shahin Khan. Reuters reports that Chinese company Tencent has agreed to lease access to roughly 100,000 advanced AI accelerators from Oracle in a five-year deal worth about 7 billion. The processors remain outside of China in Oracle data centers across Southeast Asia. So a Chinese company restricted from buying some of the world's most advanced processors can potentially rent the computation offshore.

That's the architecture we've discussed before. On-prem can mean domestic, while cloud can mean offshore. Export controls still constrain China's ability to build unlimited amounts of advanced compute domestically. But if offshore cloud access remains available, the control merely restricts ownership and location rather than access to the computation.

Posing that path would be considerably difficult to enforce, and would involve allies, cloud providers, and cross-border data policy.

Shahin Khan (1:25)

Yeah, it also marks a distinction between control of the infrastructure and control of the model and the data.

And this is a good segue into all the models that are out there. I think it's time to take stock of what that scene is internationally. At the organizational level, the United States has Anthropic, which is noted for coding and agents, Google DeepMind for multimodal and science. I mean, they all do everything, but this is kind of what comes up as their visible strength. Open AI for reasoning and agents, SpaceX slash XAI also for coding and agents, Meta pursuing a more open ecosystem approach, Microsoft AI, which increasingly has its own models across reasoning, voice, vision and agents. They aren't all sitting at the same point on the frontier, let's say, but they're all independently building a substantial model technology.

If you're not tracking China, you might be surprised with the breadth of the field there. Quen from Alibaba with broad and open weights, DeepSeq, which we all have heard of, focused on efficiency and open weights, Minimax, noted for agentic and low cost operations, ZAI, they have a system called GLM for open reasoning, Moonshot and Kimi for long context and agents, Seed from ByteDance for multimodal agents, Tencent's Honyuan for full stack deployment, and Baidu's Ernie, the domestic platform ecosystem. In Europe, France's Mistral remains the most obvious general purpose contender, with a focus on openness and efficiency and subject to European data regulations too. Then there is another category that I wouldn't call Frontier LLMs, but which could be strategically important. France's Ami Labs pursuing a novel strategy called World Models. That could be the next big thing. Canada's Cohere, Driving Enterprise and Sovereign AI. Korea's NAVER, focused on sovereignty and Korean language models. Korea's LG AI Research has something called ExoOne, focused on industrial and scientific AI.

The UAE has Falcon, working on compact reasoning. Japan's Sankana, noted for model evolution and orchestration. India's Sarvam, with a focus on Indian languages. And Israel's AI21, driving hybrid architectures. You could say that below these largest models, there is another interesting race in small language models that might find their way into bigger AI systems. Microsoft is pursuing fewer bits with the 1.58-bit Bitnet. And also its PHY family of small models and highly optimized computer use agents with FARA, F-A-R-A. The US-based PrismML, we've covered them here, a Caltech spinoff, is going further with a one-bit model. Google has Gemma, also for smaller local deployments. UAE's TII is for compact reasoning models and Canada's Cohere also has agent decoding models with only a few billion active parameters.

It's also noteworthy that for a technology as complex as AI, there are so many players globally. So important differences are going to be a level or two deeper, which also means the value of having the single highest scoring model is gradually declining. The big walk away for me is this. Once there are 10 or 15 models that are sufficiently capable, competitive advantage moves towards proprietary data, domain expertise, application integration, deployment economics, and the ability to choose the right model for the right task.

Doug Black (5:21)

Pentagon plans to create something called the Autonomous Warfare Command or Autowarcom, focused on autonomous and robotic systems across the military. It is scheduled for Q4, 2027, although some of those authorities require congressional approval.

Autowarcom would be another combatant command, but organized around a function, which is autonomous warfare rather than a geographic region or physical domain. We'd be interested in the computing architecture behind this mission. HPC and cloud systems train models, run simulations, generate synthetic environments, fuse intelligence, and update fleets. At the other end, you have HPC at the edge. Transportable supercomputers, drones, vehicles, ships, and robots need local perception, sensor fusion, navigation, and decision-making. And they cannot depend on continuous network access.