Google TPU 8t and TPU 8i: The Agentic-Era Chip Split
April 23, 2026
Google unveiled TPU 8t (Sunfish) and TPU 8i (Zebrafish) at Cloud Next 2026 — eighth-gen chips splitting AI training from inference at 2.7x price-perf.
#ai-infrastructure
Amazon-Anthropic $100B Deal: 5GW of AWS Trainium Compute
April 22, 2026
Amazon invests up to $25 billion in Anthropic as Anthropic commits $100 billion to AWS over 10 years for up to 5 GW of Trainium2 and Trainium3 compute.
Meta-Broadcom MTIA Deal: 1GW of 2nm Custom AI Silicon
April 21, 2026
Meta and Broadcom extended their MTIA chip partnership through 2029, starting with over 1 GW of custom silicon on the industry's first 2nm AI accelerator.
Cerebras IPO 2026: The $25B Nvidia Challenger
April 17, 2026
Cerebras targets a $22–25B Nasdaq listing backed by a $10B OpenAI contract. Inside the wafer-scale chip 57x bigger than Nvidia's H100 and what's at stake.
The $700B AI Infrastructure Race: Who Wins in 2026?
March 30, 2026
Big Tech is spending $700B on AI infrastructure in 2026. Here is what Amazon, Google, Meta, Microsoft, and Oracle are building and whether it will pay off.
GPU Cloud TCO 2026: Hidden Fees, Egress Costs, Real Spend
March 28, 2026
Beyond hourly rates — true GPU cloud TCO in 2026: egress, storage, commitment discounts, and what AI training and inference actually cost per month.
GPU Cloud Comparison 2026: RunPod, Vast.ai & Thunder vs AWS
March 28, 2026
Specialized GPU clouds cost 60–85% less than AWS. RunPod, Vast.ai, Thunder Compute, and Northflank benchmarked for AI training and inference in 2026.
The Custom AI Chip Race in 2026: Meta, Google, Amazon, and Microsoft vs. Nvidia
March 25, 2026
The custom AI chip race in 2026 — Meta MTIA, Google TPU, Amazon Trainium, Microsoft Maia vs. Nvidia. Strategy, cost, supply, and workload fit for each.
Mastering ML Model Training: From Costs to Code
March 22, 2026
ML model training from costs to code: frontier costs (Gemini Ultra $191M), compute doubling every 6 months, plus runnable training patterns for smaller teams.
AI Rate Limiting: Managing Fairness, Cost, and Scale in Intelligent Systems
February 2, 2026
AI rate limiting in 2026: adaptive, context-aware limits across prompts, tokens, users, and cost. The patterns that balance fairness and runaway spend.
Model Serving Patterns: From Batch to Real-Time Inference
January 28, 2026
Model serving patterns: batch, online, streaming, edge. Latency, cost, and throughput trade-offs for each — plus the tools (BentoML, vLLM, TGI) to ship with.
Mastering Model Monitoring Systems: Keeping Your ML Models Honest
January 22, 2026
ML model monitoring: detect data drift, concept drift, and fairness regressions before they hit users. Tools, dashboards, and alerts that catch early.
Cutting LLM Costs Without Cutting Corners: Practical Strategies That Work
December 14, 2025
Cut LLM costs without cutting corners: quantization, distillation, caching, batching, router choice, and infrastructure moves that actually preserve quality.
SQLite in 2025: The Unsung Hero Powering Modern Apps
December 11, 2025
SQLite in 2026, beyond embedded: edge compute, AI inference caching, local-first apps on Turso and Cloudflare D1. Why it's the database quietly powering 2026.
The Future of LLMs and Fine‑Tuning: From Foundation Models to Custom Intelligence
December 4, 2025
The future of LLMs and fine-tuning: LoRA, adapters, RAG, synthetic data, and the modular techniques replacing full retraining in 2026 production workflows.
How to MLOps: Building Reliable, Scalable Machine Learning Systems
November 29, 2025
A hands-on, deeply detailed guide to mastering MLOps—from model versioning and CI/CD to monitoring, scaling, and real-world production practices.