An audit of OpenAI Jalapeño's first benchmarks: what the 1.5–1.9x performance-per-watt lead proves, what it omits, and why full-stack inference is the
AI infrastructure balance-sheet risk starts when guarantees, leases, SPVs, and collateral depend on the same demand signal.
Meta GEM training efficiency shows why LLM-scale recommenders need workload-specific kernels, precision, parallelism, memory, and profiling.
Training a frontier AI model in 2026 requires tens of thousands of GPUs working in tight synchronization for months. Yet the factor that most often li
"OpenAI's Stargate project spans 7 gigawatts across six sites with over $400 billion in planned investment. Here is the full picture of who is buildin