DeepInfer makes determinism a testable inference feature. Audit batch invariance, seed conditions, hardware scope, and serving gaps before deployment.
Muse Spark Open computes with 42B active parameters but must keep an 816B model available. This guide budgets weights, KV cache, memory, and topology.
DeepSeek's Ascend ports show where AI infrastructure compatibility ends: reuse APIs, budget for layouts, compilers, kernels, networks, and validation.
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