Week of March 10, 2026: AI Inference Costs Plummet 60% as New Silicon Enters Market

Executive Summary

A wave of custom AI inference chips from AWS (Trainium3), Google (TPU v6), and Microsoft (Maia 2) has driven inference costs down 60% year-over-year. NVIDIA responded by cutting H200 pricing and announcing aggressive B300 availability timelines. The cost to run a frontier AI model has fallen below $0.001 per 1K tokens on optimized infrastructure.

Key Takeaways

1. AI inference costs down 60% YoY driven by custom silicon competition
2. AWS Trainium3 delivers 3x price-performance improvement over NVIDIA H100 for transformer models
3. Google TPU v6 pods now available in 12 regions with automatic right-sizing
4. NVIDIA cuts H200 cloud pricing 25% and accelerates B300 availability to Q2 2026
5. FinOps teams should audit AI workload placement: significant savings available through provider-native silicon

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