APAC inference placement decides latency and cost more than model choice. Compare Mumbai, Singapore and Tokyo under residency, volume and ops limits.
Open GPU kernel modules improve debuggability and auditability for fleet operators, not performance. Here is who benefits and who can ignore them.
AirLLM runs 70B models on a 4GB GPU. The trick is not the sharding; it is that enterprise inference workloads break the edge cost model. Here is why GPU accessibility is not the same thing as cost efficiency.
SCALE and other CUDA-compatibility layers are cracking Nvidia's software moat, letting unmodified CUDA binaries run on AMD hardware — though performance parity still isn't guaranteed. Here is what it means for AI inference costs and enterprise infrastructure in 2026.
Serverless inference promises pay-per-request economics but the five-second cold start destroys the user experience. Here is what actually works: persistent model workers, speculative warmers, hybrid architectures, and the infrastructure patterns that let you keep serverless pricing without paying the latency tax.
Your data location is no longer an afterthought. When every cloud provider promises the best AI infrastructure, the real tiebreaker is where your company's enterprise data already lives. We explore how data gravity shapes vendor selection, transfer costs, and the architecture of your AI strategy.