Google TPU: The Largest Alternative Ecosystem

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

1. Own chip design — Full control over architecture
2. Own frameworks — JAX optimized for TPU
3. Own models — Gemini trained on TPU
4. 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.