Google proves a stable non-NVIDIA path exists—but requires commitment to a different ecosystem (JAX vs CUDA).
TPU v7 Ironwood (2025)
- Performance: 4.6x vs TPU v5
- Memory: HBM3E Equipped
- Scale: 9,216 chip pods
- Best cost/performance: $5,579 per H100-equivalent
Key Metrics
- Compute Share: 13.3%
- Revenue Share: 7.8%
- Revenue: $23.9B
- Cost/H100e: $5,579 (best value)
Value Proposition vs NVIDIA
- Price Advantage: 3x cheaper ($5,579 vs $17,000)
- Trade-off: JAX ecosystem vs CUDA ubiquity
TPU Evolution
v4 (2022) → v5e (2023) → v6e (2024) → v6e Trillium (2025) → v7 Ironwood (2025)
2025 Infrastructure Investment
- $75B CapEx
- Gemini Infrastructure
- TPU v7 Rollout
Vertical Integration Advantage
- Own chip design — Full control over architecture
- Own frameworks — JAX optimized for TPU
- Own models — Gemini trained on TPU
- Own cloud — GCP infrastructure
Strategic Use Cases
- Internal Training: Gemini 2.0 models 100% on TPU
- Cloud Offering: GCP AI Platform for external customers
- Search & Ads: AI-powered results, massive inference
Limitations
- Ecosystem Lock: JAX-centric, not PyTorch
- Availability: Primarily GCP-only
- Adoption: Smaller dev ecosystem
Strategic Position: Best Value Alternative. Google proves stable non-NVIDIA path exists—but requires commitment to different ecosystem.
This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.
