TorchTPU marks a seismic shift in AI infrastructure, bridging the gap between PyTorch’s flexibility and Google’s specialized silicon. By neutralizing Nvidia’s software dominance, this initiative offers developers a high-performance “escape hatch” to scale massive models more efficiently.
NVIDIA’s acquisition of SchedMD marks a turning point for HPC. While Slurm remains open-source, the community is weighing its future. We dive into the history of Slurm and the viable alternatives for a vendor-neutral AI infrastructure.
Building an AI Factory demands a fortress-like structure designed for continuous operation and heavy loads. This comprehensive guide walks through the complex, multi-phase construction journey, from site acquisition and early procurement of long-lead items, to the final commissioning of shell, core, power, and cooling systems.
The 1,100W AI chip has sparked a heat crisis. As traditional air cooling fails, the 200 MW AI Factory shifts to Direct Liquid Cooling (DLC), sophisticated plumbing with CDUs, and new immersion technologies to manage unprecedented thermal loads.
The AI Factory’s colossal power demand is straining the U.S. grid. We detail how 200 MW data centers secure power, from evaluating new sources like SMRs to navigating the multi-year procurement bottlenecks of electrical infrastructure.
AI factories are emerging as the industrial backbone of the AI era. Demand for high density GPU clusters is surging, but power, cooling, and multi-year equipment lead times have become the real constraints shaping the future of large scale compute.
Google is positioned to exploit the growing power gap in AI infrastructure. As Nvidia struggles with high power density requirements, Google can flood the market with efficient TPUs that deploy in existing data centers and reshape AI chip economics.
The AI infrastructure market is fracturing under recession pressures, runaway memory shortages, and a surge of new competitors. Our Q4 2025 snapshot breaks down the chaos, highlights major shifts, and tracks how the industry is reshaping itself in real time.
ZLUDA 5 is emerging as the most credible open source challenge to CUDA by enabling unmodified CUDA applications to run on non NVIDIA GPUs with near native performance. It reshapes the competitive landscape across AI inference, HPC, and GPU virtualization.
MinIO’s Community Edition is in maintenance mode, sparking backlash. Many popular open-source projects have shifted commercial. Explore alternatives like Ceph, SeaweedFS, and Garage in this in-depth comparison.
Tensormesh introduces a distributed KV cache system that reduces redundant LLM prefill computation, improving throughput and lowering inference costs. A technical look at how cross-request tensor reuse changes large-scale AI serving.
Luminal is rethinking AI inference by building a high-performance compiler and serverless platform. By automating model optimization for GPUs and accelerators, teams can reach peak performance without manual kernel tuning, reducing cost and complexity.
Explore how open-source embedding models like BGE, E5-Large, and MiniLM are reshaping retrieval and RAG systems. Learn how they integrate with pgvector and vector databases to power enterprise-grade search, analytics, and AI infrastructure.
Spectral Compute is transforming GPU programming with SCALE, a toolkit that allows CUDA applications to run on AMD GPUs without code changes. By breaking vendor lock-in, it enables faster, cost-effective AI and high-performance computing workflows.
Reinforcement pre-training applies reinforcement learning principles early in model development to guide reasoning, exploration, and safety behaviors. By shaping learning before alignment, it bridges the gap between pre-training and RLHF—enhancing intelligence formation in large AI systems.
