Author name: Bijit Ghosh

TL;DR Enterprises face 3× more AI workloads than available GPUs heading into 2026 Traditional schedulers fail heterogeneous workloads (training + inference + RAG + analytics) Software like Kubex can unlock significant GPU capacity gains using MIG + dynamic fragmentationPower—not GPUs—will be the binding constraint by 2027. The GPU Supply Crisis (2026 Reality Check) By 2026, …

GPU Fragmentation Is Killing AI Economics Read More »

Densify is rewriting how GPUs are allocated, shared and scaled across every layer of AI infrastructure The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU …

Automated, Autonomous GPU Optimization Read More »