DATA Act 2026: Off-Grid Power for AI Data Centers
Senator Cotton's DATA Act exempts off-grid data centers from FERC. Analysis of regulatory bypass, interconnection queue avoidance, and infrastructure implications.
Blog
Insights on GPU infrastructure, AI, and data centers.
SpaceX Files for Million-Satellite Orbital Data Center
SpaceX filed FCC plans for 1M orbital data center satellites projecting 100GW AI compute. Analysis of technical specs, regulatory challenges, and market implications.
GPT-5.2 Crosses 90% ARC-AGI: Infrastructure Implications
GPT-5.2 achieves 90% ARC-AGI-1 and perfect AIME 2025. Analysis of benchmark results and data center infrastructure requirements for inference.
NVIDIA NIM and Inference Microservices: Deploying AI at Enterprise Scale
Deploying a large language model used to require weeks of infrastructure work, custom optimization scripts, and a team of ML engineers who understood the dark arts of inference tuning. NVIDIA changed
APAC Data Center Power Crisis: Solutions for 200 TWh AI Energy Demand
APAC faces 165% power demand increase by 2030. Singapore restricts data centers, Malaysia faces blackouts. Solutions from microgrids to SMRs for AI infrastructure.
NVIDIA Blackwell Ultra and B300: what the next GPU generation demands
The NVIDIA Blackwell Ultra GPU delivers 15 petaflops of dense FP4 compute, 50% more memory than the B200, and 1.5 times faster performance.¹ A single GB300 NVL72 rack achieves 1.1 exaflops of FP4
GPU Virtualization Performance: Optimizing vGPU for Multi-Tenant AI Workloads
Alibaba Cloud discovered their vGPU deployment achieving only 47% of bare-metal performance despite marketing claims of 95% efficiency, costing them $73 million in over-provisioned infrastructure to
How DeepSeek and Qwen change AI infrastructure economics
DeepSeek claims to have trained its R1 model for just $5.6 million using 2,000 NVIDIA H800 GPUs.¹ Comparable Western models required $80 million to $100 million and 16,000 H100 GPUs.² The January
CXL Memory Expansion: Breaking the Memory Wall in AI Data Centers
Memory bottlenecks kill AI performance. Large language models routinely exceed 80 to 120GB per GPU for KV cache alone, overwhelming even the most expensive HBM-equipped accelerators.¹ Compute Express
Google TPU vs NVIDIA GPU: An Infrastructure Decision Framework for 2025
Anthropic closed the largest TPU deal in Google's history in November 2025—committing to hundreds of thousands of Trillium TPUs in 2026, scaling toward one million by 2027.¹ The company that built
Object Storage for AI: Implementing GPU Direct Storage with 200GB/s Throughput
Meta achieved a 3.8x improvement in model training speed by implementing GPUDirect Storage across their research clusters, eliminating the CPU bottleneck that previously limited data loading to
Model Serving Optimization: Quantization, Pruning, and Distillation for Inference
A single GPT-3 inference request costs $0.06 at full precision but drops to $0.015 after optimization, a 75% reduction that transforms AI economics at scale. Model serving optimization techniques