According to Sawyer Merritt on X, a new image post drew attention to Tesla’s AI stack and data collection, highlighting the role of on-vehicle compute and centralized training. As reported by Tesla’s 2023–2024 AI Day materials and earnings calls, Tesla is investing in Dojo to scale video model training for Full Self-Driving with billions of real-world miles as training data. According to Tesla’s 2024 Q4 update, the company continues to expand its autolabeled video datasets and multi-camera neural networks for end-to-end driving. Based on The Information’s reporting, Tesla is procuring Nvidia H100 clusters in parallel with Dojo for model training throughput. These developments create five business implications: 1) lower per-mile data acquisition costs through fleet learning; 2) faster iteration on end-to-end driving models via vertically integrated training; 3) potential licensing of autonomy stacks to OEMs once safety metrics are validated; 4) margin expansion from software subscriptions such as FSD; and 5) defensible moat from proprietary, large-scale driving video corpora. All statements are drawn from the above sources; the image post by Sawyer Merritt serves as a topical pointer to Tesla’s ongoing AI strategy.

According to Andrej Karpathy on Twitter, enabling FP8 precision training for GPT-2 using H100 GPUs has resulted in a 4.3% improvement in training time, reducing it to just 2.91 hours. Karpathy highlights that with 8xH100 spot instance pricing, the total cost to reproduce the GPT-2 model now stands at approximately $20. This marks a dramatic cost reduction compared to OpenAI's original $43,000 GPT-2 training seven years ago. As reported by Karpathy, further optimization using techniques such as Flash Attention 3 kernels, the Muon optimizer, and advanced attention patterns have contributed to these gains. While FP8 offers theoretical FLOPS advantages, Karpathy notes practical challenges including overhead from scale conversions and limited support, especially at the GPT-2 model scale. Nonetheless, the industry shift to FP8 hints at broader opportunities for cost-effective LLM training, as evidenced by torchao's reported 25% speedup on larger models like Llama3-8B. According to Karpathy, continued improvements in FP8 application and model training strategies can reduce both time and financial barriers for LLM development, opening further business and research opportunities.

According to Andrej Karpathy on Twitter, enabling FP8 training has improved 'time to GPT-2' by 4.3%, reducing the training duration to 2.91 hours on an 8x H100 GPU setup. Karpathy notes that, using spot instance pricing, the cost to reproduce GPT-2 training is now approximately $20. This marks a significant shift from GPT-2's original classification as 'too dangerous to release' in 2019 to being as accessible as MNIST today. The FP8 implementation presented practical challenges, with support limitations and real-world performance falling short of theoretical FLOPS gains. For tensorwise scaling, a speedup of about 7.3% was achieved, though Karpathy highlights that further optimizations could lower the time and cost even more. Comparatively, torchao reported a 25% speedup for Llama3-8B training using FP8. Karpathy also underscores that, thanks to advancements like Flash Attention 3 and the Muon optimizer, the cost of training GPT-2 has dropped nearly 600 times over the past seven years, offering substantial business opportunities for AI startups and researchers seeking low-cost, rapid model prototyping. As reported by Karpathy, ongoing optimizations in projects like nanochat continue to drive down training costs and times, making advanced language model training accessible to a wider audience.

According to Sawyer Merritt, Tesla announced plans to more than double the size of its onsite compute resources in Texas by the first half of 2026, measured in H100 GPU equivalents. The company aims to maximize capital efficiency by scaling its AI training infrastructure strategically, addressing training backlogs and future compute demands. This expansion signals Tesla's commitment to advancing AI-powered autonomous technologies, with significant implications for AI model training and business scalability, as reported by Sawyer Merritt on Twitter.