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Google's TPUs vs. NVIDIA and AMD GPUs: Why The Competition Matters
- Elias Zeekeh, MBA, CPA, CMA
- 52 minutes ago
- 5 min read
Google [finance:Alphabet Inc.] is quietly building a serious challenge to NVIDIA [finance:NVIDIA Corporation] and AMD [finance:Advanced Micro Devices, Inc.] dominance in AI chips. While NVIDIA controls roughly 80% of the AI accelerator market, Google's Tensor Processing Units (TPUs) represent a fundamentally different approach—one that could reshape how enterprises think about AI infrastructure costs.
The Architecture Divide
The key difference isn't just speed—it's purpose. TPUs use systolic array architecture, a grid of processing elements specifically optimized for matrix operations at the heart of neural networks[1]. Data flows through these arrays in a rhythmic pattern without constantly returning to memory, making them incredibly efficient for one task: deep learning.
GPUs, by contrast, use thousands of general-purpose cores designed to handle any parallel computation[2]. This flexibility is powerful, but it comes at a cost—literally and figuratively.
Think of it this way: TPUs are Formula 1 race cars, built for one track. GPUs are pickup trucks that can go anywhere but won't set any records on the straightaway.
The Performance Reality
Raw speed comparisons mislead. NVIDIA's H100 delivers 156 TFLOPs, while Google's latest Ironwood TPU hits 4,614 TFLOPs for FP8 operations[3][4]. But that's not the full story.
The real breakthrough is scalability and cost per unit of compute. TPU Ironwood can scale to 9,216 chips per pod, delivering 42.5 exaflops of combined compute—more than 24x the power of the world's largest supercomputer[3][4]. Google's optical interconnect between TPUs is dramatically cheaper and more power-efficient than NVIDIA's networking solutions[5].
More importantly: hyperscalers may obtain AI compute from TPUs at roughly 20% of the cost of high-end NVIDIA GPUs—implying a 4x-6x cost efficiency advantage per unit of compute[6]. When NVIDIA commands 80% gross margins on data center chips, that markup flows directly to customers' bills.
The Case for TPUs: Real-World Numbers
The economics are compelling. Midjourney reduced monthly compute spending from $2 million to $700,000 after migrating to TPUs—a 65% cost reduction[7]. Snap achieved 70% cost reductions through systematic TPU optimization[7].
Power efficiency matters too. TPUs deliver 2-3x better performance per watt compared to contemporary GPUs[8][9]. One former Google Cloud engineer stated that "TPU v6 is 60-65% more efficient than GPUs"[5]. At cloud scale, energy costs matter enormously.
On-demand TPU v6e starts at $1.375 per hour, dropping to $0.55 with 3-year commitments—roughly 30-50% cheaper than equivalent GPU capacity[7].
Where TPUs Actually Win: Inference
Here's the real inflection point: inference is becoming the dominant AI workload. Training large models is expensive, but serving queries to billions of users is the business.
Google positioned its newest TPU generation—Ironwood—explicitly as the company's "first TPU for the age of inference"[3]. The architecture delivers very low latency for real-time applications like Google Search, plus optimization for large-scale LLMs and Mixture-of-Experts models[3].
This matters because inference workloads are often different from training workloads. They require sustained throughput, not peak performance. They benefit from specialized optimizations, not generalpurpose compute. TPUs are tailor-made for this.
The Software Ecosystem Problem
Here's where TPUs face a real constraint: software.
NVIDIA's CUDA ecosystem is decades mature. Developers think in CUDA. PyTorch—the dominant framework in research and industry—natively supports CUDA with deep integration. Nearly every major AI/ML framework works seamlessly with NVIDIA hardware[10][11].
TPUs support TensorFlow and JAX natively but lag on PyTorch. The PyTorch/XLA bridge exists but introduces friction[10][11]. This ecosystem advantage has been one of NVIDIA's strongest moats, and it matters enormously for developers.
Additionally, TPUs are locked to Google Cloud Platform. NVIDIA and AMD GPUs deploy across any cloud, on-premises data centers, or local workstations. That flexibility counts.
Market Validation: Even Apple Uses Them
The most telling endorsement: Apple [finance:Apple Inc.] revealed it uses TPUs to train Apple Intelligence models[12][13]. With virtually unlimited resources, Apple chose TPUs for their cost and efficiency advantages. Apple's AFM model was trained on 8,192 TPU v4 chips[13].
Anthropic recently announced a landmark deal with Google, gaining access to up to one million TPUs through a partnership valued in the tens of billions of dollars[14]. Anthropic's multi-cloud strategy includes TPUs alongside Amazon's Trainium and NVIDIA GPUs, but TPUs deliver the "strongest priceperformance and efficiency," according to their financial leadership[14].
Where NVIDIA Still Dominates
Don't mistake this analysis for NVIDIA losing its edge. NVIDIA remains the default choice because:
Software maturity – The CUDA ecosystem is unmatched
Flexibility – GPUs work for virtually any computational task
Multi-cloud optionality – Not locked into one provider
Developer familiarity – Everyone knows CUDA
NVIDIA's Blackwell generation continues advancing, and the company maintains ecosystem advantages that won't disappear overnight[15].
AMD's MI300X is also competitive, particularly for inference with 192GB of HBM3 memory and opensource ROCm alternatives[16][17].
The Real Takeaway
For most organizations, this isn't an either/or choice—it's about optimization. Companies like Anthropic are building multi-accelerator strategies, using TPUs for cost-sensitive inference, GPUs for flexible workloads, and Amazon's Trainium for specialized tasks.
But for cloud providers and hyperscalers facing relentless AI infrastructure costs, TPUs represent a genuine alternative. Google's decade-long TPU investment is finally yielding competitive pressure in a market that desperately needs it.
The "NVIDIA tax" is real. And TPUs are starting to collect on it.
Disclaimer
This content is for informational and educational purposes only and does not constitute financial, investment, tax, or legal advice. You are responsible for your own investment decisions, and should consult a qualified professional who understands your individual circumstances before acting on any information presented here.
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References
[1] Google Cloud. (2018). "An in-depth look at Google's first Tensor Processing Unit (TPU)." Google Cloud Blog. https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-fi rst-tensor-processing-unit-tpu
[2] The Chip Letter. (2024, March 23). "Google's First TPU Architecture." Substack. https://thechiplette r.substack.com/p/googles-first-tpu-architecture
[3] Google Blog. (2025, April 8). "Ironwood: The first Google TPU for the age of inference." Google
[4] CloudOptimo. (2025, April 21). "Google TPU Ironwood: Revolutionizing AI Inference at Scale." http s://www.cloudoptimo.com/blog/google-tpu-ironwood-revolutionizing-ai-inference-at-scale/
[5] UncoverAlpha. (2025, November 23). "The chip made for the AI inference era – the Google TPU."https://www.uncoveralpha.com/p/the-chip-made-for-the-ai-inference
[6] NASDAQ. (2025, May 21). "Cost of AI Compute: Google's TPU Advantage vs. OpenAI's Nvidia
[7] IntroL. (2025, September 27). "Google TPU v6e vs GPU: 4x Better AI Performance Per Dollar
[8] CloudOptimo. (2025, April 14). "TPU vs GPU: What's the Difference in 2025?" https://www.cloudopt imo.com/blog/tpu-vs-gpu-what-is-the-difference-in-2025/
[9] Artech Digital. (2023, December 31). "Energy-Efficient GPU vs. TPU Allocation." https://www.artech
[10] Wevolver. (2025, September 15). "TPU vs GPU in AI: A Comprehensive Guide to Their Roles andImpact on Artificial Intelligence." https://www.wevolver.com/article/tpu-vs-gpu-in-ai-a-comprehensive-g uide-to-their-roles-and-impact-on-artificial-intelligence
[11] NWAi. (2025, October 16). "TPUs vs GPUs in AI: Complete Comparison Guide." https://nwai.co/tp us-vs-gpus-in-ai-complete-comparison-guide/
[12] CNBC. (2024, July 29). "Apple says its AI models were trained on Google's custom chips." https:// www.cnbc.com/2024/07/29/apple-says-its-ai-models-were-trained-on-googles-custom-chips-.html
[13] CNBC. (2024, August 23). "How Google makes custom cloud chips that power Apple AI andGemini." https://www.cnbc.com/video/2024/08/23/how-google-makes-custom-cloud-chips-that-powerapple-ai-and-gemini.html
[14] Bloomberg. (2025, October 23). "Google, Anthropic Announce Cloud Deal Worth Tens of Billions."https://www.bloomberg.com/news/articles/2025-10-23/google-anthropic-announce-cloud-deal-worth-te ns-of-billions
[15] CNBC. (2025, November 21). "Nvidia Blackwell, Google TPUs, AWS Trainium: Comparing the
Top AI Chips." https://www.cnbc.com/2025/11/21/nvidia-gpus-google-tpus-aws-trainium-comparing-the -top-ai-chips.html
[16] Meta and Microsoft. (2023, December 6). "Meta and Microsoft to buy AMD's new AI chip asalternative to Nvidia." CNBC. https://www.cnbc.com/2023/12/06/meta-and-microsoft-to-buy-amds-new -ai-chip-as-alternative-to-nvidia.html
[17] AMD Official Blog. (2025, April 24). "Engineering Insights: Unveiling MLPerf® Results on AMDInstinct™ MI300X." https://www.amd.com/en/blogs/2024/engineering-insights-unveiling-mlperf-resultson.html