According to Sawyer Merritt on Twitter, Tesla plans to add $4 billion to $5 billion in AI training capacity in Q2 2026, utilizing approximately 155,000 Nvidia H100 GPUs. This move highlights Tesla's continued investment in large-scale AI infrastructure to advance its autonomous driving and robotics initiatives. As reported by Sawyer Merritt, this scale of GPU deployment positions Tesla among the top global buyers of advanced AI hardware, offering significant business opportunities for Nvidia and reinforcing the growing arms race for AI compute power within the automotive sector.

According to @AdiOltean on Twitter, nanoGPT has become the first large language model (LLM) to be trained and used for inference entirely in space, leveraging an Nvidia H100 GPU aboard the Starcloud-1 satellite (source: https://x.com/AdiOltean/status/1998769997431058927). The Starcloud team successfully trained nanoGPT, based on Andrej Karpathy's architecture, on the complete works of Shakespeare and demonstrated inference capabilities on both nanoGPT and a preloaded Gemma model. This milestone highlights the potential to shift high-performance AI workloads off Earth, utilizing space-based resources and abundant solar energy. The success sets the stage for new business opportunities in AI-powered satellite computing, distributed cloud infrastructure, and green AI innovation (source: @karpathy).