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.
How the Model Context Protocol is becoming the universal interoperability layer for agentic AI, and why its donation to the Agentic AI Foundation marks a Kubernetes-level inflection point for enterprise adoption.
SCALE and other CUDA-compatibility layers are cracking Nvidia's software moat, letting unmodified CUDA binaries run on AMD hardware. Here is what it means for AI inference costs and enterprise infrastructure in 2026.
Native K8s orchestration is evolving to handle GPU scheduling, checkpointing, and live migration at the scale that AI demands.
Inference cost architecture: how smart model routing between frontier and distilled models creates real margin at scale. Unit economics, production examples, and the infrastructure decisions that determine profitability.
Analyzing the bottleneck of bulk clustering and using exact-match caching to reduce index compute load.