Show HN: Solving the H100 OOM Wall with CTDR – Maxwell Dashboard Included (github.com)

A new tool called CTDR has been introduced to address the "H100 OOM Wall," a limitation faced by many in the AI/ML community due to the maximum 80GB HBM3 memory on H100 GPUs. The tool enables efficient NxN materialization even at a massive scale of N=500,000+, achieving a remarkable 90.4% streaming multiprocessor utilization. This is significant as standard FP16 materialization becomes impractical at such scales, but CTDR offers a solution that reduces energy consumption per query by 70% and eliminates hallucinations through the use of deterministic p-adic invariants. The CTDR includes a Maxwell Dashboard for users to run audits and assess their GPU resource utilization, demonstrating over 100x efficiency gains in materialization at scale. The dashboard allows infrastructure owners to compare GPU receipts and verify results while providing insights into deterministic reasoning and GPU efficiency strategies. Lead Engineer Stanislav Byriukov emphasizes the need for technical discussions around this innovative approach, focusing on improving memory orchestration and computational density, which could transform how AI models are deployed in resource-intensive environments.