A factory digital twin runs three GPU workloads at once: rendering, physics, and AI. All three compete for the same GPU memory. NVIDIA’s engineering-twin blueprint needs 16 GB to show pre-computed results, and two 40 GB GPUs to run the AI physics live. BMW Group already runs twins of 30+ production sites, cutting collision checks from almost four weeks to three days. Our new article explains what a twin is for AI builders, and how to size the cluster for the live twin, not the demo. Full article below.

Today, Axe Compute (NASDAQ: $AGPU ) rings the @Nasdaq Opening Bell from Times Square. Watch live at 9:30 a.m. ET as we mark an important milestone in Axe Compute’s growth and continued expansion in AI infrastructure. #AxeCompute #Nasdaq

Vera Rubin or Blackwell? We updated our comparison with NVIDIA’s latest specs. Where Vera Rubin earns its place: • Agents that plan across many steps and hold memory for the whole task. • Long-context serving: coding assistants over a full repository, research agents, document and log analysis at a million tokens. • Mixture-of-experts at production scale, where expert routing leans on the interconnect. • High-volume copilots and search, where cost per token decides the margin. Where Blackwell stays the better buy: • Training and fine-tuning that cannot wait, on a mature software stack shipping in volume today. • Dense, short-context inference: chatbots, classification, ranking, recommendation, embeddings, moderation. • Cost-sensitive inference across fraud detection, medical imaging, industrial vision, and transcoding. The two run side by side. Axe Compute Build delivers dedicated, single-tenant clusters on Vera Rubin NVL72, GB300 NVL72, and B300, on a 36 or 60 month term with a committed delivery date and a mid-term GPU upgrade path, so capacity can move to Rubin as the workload calls for it. Full report in the replies.

Axe Compute (NASDAQ: $AGPU) will ring the @Nasdaq Opening Bell on October 12 at the Nasdaq MarketSite in Times Square. The milestone follows significant commercial growth, including more than $3.45B in signed contracts and continued expansion of dedicated GPU compute capacity.

Before 2010, AI training compute doubled every 20 months. Then it doubled every 6 months. Then every 3.4 months, a rise of more than 300,000 times in six years. Now reasoning models have added a second axis: compute spent at inference, on top of training. Full timeline in the comments.

Video games are becoming a serious training ground for robots. General Intuition raised a $133.7 million seed round on that idea. Gameplay records an action and exactly what followed, and games already encode what makes physical space hard to model: objects persist, occlude each other, and move under causal rules. It points at a bigger shift. Systems that act in the world need a persistent model of it, and that turns bursty inference into sustained, stateful GPU workloads. Axe Compute lets teams pick the platform, region, and term, on single-tenant bare metal with zero egress fees. That matters when a pipeline moves petabytes of video between stages. Read our full piece on spatial AI. Link in the comments.

What happens when data center growth slows down? When data center capacity can’t keep pace with demand, access to the compute that powers AI can become a bottleneck for research, discovery and innovation. Chris Miglino's answer on @FoxBusiness was direct: "When we slow down AI infrastructure, we slow down the cure to cancer." AI infrastructure is becoming critical to research, innovation and building what comes next. $AGPU