xAI's Memphis Colossus: Anatomy of a 100,000 GPU Supercomputer
xAI built 100,000 GPU Colossus cluster in 122 days, doubled to 200K in 92 more. 250MW power, Spectrum-X Ethernet. Inside the world's largest AI supercomputer.
Blog
Insights on GPU infrastructure, AI, and data centers.
Amazon's Trainium3 throws down the gauntlet in the AI chip wars
AWS launched Trainium3 UltraServers at re:Invent 2025, and the specifications demand attention. Built on TSMC's 3nm process, each Trainium3 chip delivers 2.52 petaflops of FP8 compute with 144GB of
CoreWeave Deep Dive: How a Former Crypto Miner Became AI's Essential Cloud
OpenAI chose CoreWeave over AWS for $22.4B in infrastructure. Learn how this former crypto miner became the GPU cloud powering frontier AI development.
800G Networking for AI: Planning Your Next-Generation GPU Fabric
800G dominates AI cluster switch shipments in 2025. NVIDIA networking revenue doubles to $7.3B. Planning the migration from 400G to 800G and beyond.
Video Generation AI Infrastructure: Building for Sora-Scale Models
Generating a single 10-second video with AI models consumes GPU resources equivalent to thousands of ChatGPT queries.¹ The computational intensity explains why video generation costs range from $0.50
Zero-Downtime Data Center Migration: Complete Playbook for GPU Clusters
Moving 10,000 GPUs between data centers while maintaining continuous AI training sounds impossible until you learn that Meta accomplished exactly this feat during their 2023 facility consolidation,
Carbon Accounting for AI Workloads: Measuring and Reporting GPU Emissions
NVIDIA published the Product Carbon Footprint for H100 baseboard with eight H100 SXM cards, estimating embodied emissions at 1,312 kg CO2e—approximately 164 kg CO2e per card. Memory contributes
CXL 4.0 Infrastructure Planning Guide: Memory Pooling for AI at Scale
Complete CXL 4.0 deployment guide covering bundled ports, multi-rack memory pooling, KV cache offloading, vendor ecosystem, and 2026-2027 planning timeline.
Model Versioning Infrastructure: Managing ML Artifacts at Scale
MLflow 3.0 extended its model registry to handle generative AI applications and AI agents, connecting models to exact code versions, prompt configurations, evaluation runs, and deployment metadata.¹
Federated Learning Infrastructure: Privacy-Preserving Enterprise AI
KAIST researchers developed a federated learning method that enables hospitals and banks to train AI models without sharing personal information.¹ The approach uses synthetic data representing core
InfiniBand vs Ethernet for GPU Clusters: 800G Network Architecture Decision Guide
InfiniBand delivers 15% better performance but costs 2.3x more than Ethernet. Learn how Meta, OpenAI, and Google chose their $50M network architectures.
GPU Cluster Monitoring: Real-Time Performance Analytics and Predictive Maintenance
Tesla's Dojo supercomputer monitors 3,000 custom D1 chips generating 4.2 billion metrics per second, using machine learning models that predict hardware failures 72 hours before they occur with 94%