A few years ago, Caltech Prof. and co-founder of Accelerated Understanding, Anima Anandkumar set out to develop the first open-source weather model with AI. Talking to experts in the field, she was met with skepticism. Weather is chaotic, physics simulations are hard, have been developed for decades, and require supercomputers, the data just isn’t there. Despite reservations, Anima went forth and built. Within a year her team had developed FourCastNet, a predictive model that is competitive with the best physics-based simulations available. Thanks to Anima, and her follow up work, anyone can now predict weather accurately over a short timescale using consumer grade GPUs.1

In the fifteen or so science episodes we’ve released on Latent.Space, we’ve covered atoms, molecules, materials, biology, and math. Anima is a pioneer in studying physical systems that are continuous. Weather, fusion,…

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This release features 584 commits from 270 contributors (76 new)!

Pre-built release artifacts are available in the Assets section at the bottom of this page, including:

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The fact that AI wrote 1M LOC and then refined it over the course of the next couple of months to produce a reliable piece of software that is currently running on millions of developer machines is absolutely mind blowing. And you can say, “well it’s not that impressive because they had an oracle to compare against, so it was simple to go from one language to another”, but I think that’s selling this entire thing short. If you can build a verification system and give proper direction, AI can produce a highly complex, highly sophisticated piece of software and it can continue to refine it until it just works.

This is a quotation collected by Simon Willison, posted on 26th August 2026.

When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels, and capturing NVIDIA CUDA graphs. For large models, initialization can take several minutes, during which surviving workers must absorb the displaced traffic.

Shadow engine recovery, available as a preview feature in NVIDIA Dynamo, moves most of this recovery work off the serving path. It keeps a fully initialized shadow engine idle on the same GPUs as the active engine. The GPU Memory Service (GMS) shares the existing weights between the engines without creating another copy in HBM. If the active process fails, the shadow takes over within seconds. Re-initialization occurs in the background entirely off the serving path.

We measured the impact by deliberately terminating one worker in a two-worker GLM-5.2 deployment. Without shad…

Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with databases, and analyze results before returning information to the model. As these loops run concurrently across an AI factory, CPU performance increasingly shapes both per-agent responsiveness and overall factory throughput.

This creates a different CPU design point. Agent workloads depend on:

These requirements differ from traditional cloud CPU designs, which often prioritize core density and throughput across more uniform workloads. Agentic AI instead places greater value on sustained per-thread progress across the sequential, branch-heavy, and latency-sensitive software paths within each agent loop.

NVIDIA Vera CPU was built for this class of workload, with the Olympus core at the center of its design. This post examines the…