Agentic Inference Benchmarks in 2026: Agents per Megawatt
MLPerf and Artificial Analysis both shipped an agentic inference benchmark in 2026. What agents per megawatt measures, which hardware leads, and the caveats.
MLPerf and Artificial Analysis both shipped an agentic inference benchmark in 2026. What agents per megawatt measures, which hardware leads, and the caveats.
When the US government pulled Claude Fable 5 offline, single-provider apps broke. Build a multi-provider LLM fallback chain that survives a model recall.
SpaceX begins trading as SPCX after a record $75B IPO. How the xAI merger, Colossus, and billion-dollar compute deals built an AI infrastructure giant.
Deploy LiteLLM Proxy v1.85 in production: Docker Compose, Postgres, virtual keys with budgets, fallback routing, and cost tracking for Claude, GPT, Gemini.
Google commits up to $40B to Anthropic — $10B now at $350B valuation plus 5GW of TPU capacity over five years. Big Tech's latest AI infrastructure bet.
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.
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 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 priced its Nasdaq IPO at $185/share, valuing the company at $56.4B, backed by a $10B+ OpenAI contract. Inside the wafer-scale chip 57x bigger than Nvidia's H100.
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.
Beyond hourly rates — true GPU cloud TCO in 2026: egress, storage, commitment discounts, and what AI training and inference actually cost per month.
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 MTIA, Google TPU, Amazon Trainium, Microsoft Maia vs. Nvidia. Strategy, cost, supply, and workload fit for each.
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 in 2026: adaptive, context-aware limits across prompts, tokens, users, and cost. The patterns that balance fairness and runaway spend.
Model serving patterns: batch, online, streaming, edge. Latency, cost, and throughput trade-offs for each — plus the tools (BentoML, vLLM, TGI) to ship with.
ML model monitoring: detect data drift, concept drift, and fairness regressions before they hit users. Tools, dashboards, and alerts that catch early.
Cut LLM costs without cutting corners: quantization, distillation, caching, batching, router choice, and infrastructure moves that actually preserve quality.
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: LoRA, adapters, RAG, synthetic data, and the modular techniques replacing full retraining in 2026 production workflows.
A hands-on, deeply detailed guide to mastering MLOps—from model versioning and CI/CD to monitoring, scaling, and real-world production practices.