For years, NVIDIA GPUs were the default choice for ML researchers and engineers. But in 2024–2025, Google’s Tensor Processing Units (TPUs) began gaining real traction. This post explains what each accelerator does, how they differ in practice, and the concrete reasons TPUs are suddenly being chosen for large-scale AI training and inference.
What exactly are TPUs and GPUs?
NVIDIA GPUs are general-purpose parallel processors originally designed for graphics. Over the last decade NVIDIA built a robust ecosystem (CUDA, cuDNN, Triton) and dominated AI compute. Google TPUs are custom ASICs optimized specifically for tensor math—matrix multiplications and convolutions that power deep learning.
TPU vs GPU — Practical differences
| Feature | NVIDIA GPU | Google TPU |
|---|---|---|
| Architecture | General-purpose parallel processor | ASIC optimized for tensor ops |
| Performance / Watt | High | Often higher due to specialization |
| Software ecosystem | Extensive (CUDA, PyTorch, TensorRT) | Growing (JAX, PyTorch/XLA, XLA) |
| Scalability | Excellent for mixed workloads | Exceptional for large-scale model parallelism |
| Best use-cases | Flexible workloads, on-prem deployments | Large transformer training & high-scale inference |
Why TPUs are suddenly gaining traction
1. GPU scarcity created a vacuum
Supply issues and long waitlists for H100 and similar chips pushed teams to seek alternatives. TPUs, available through Google Cloud, became a practical option for teams that could not wait months for GPUs.
2. Cost-per-performance started to favor TPUs at scale
For very large model training runs and inference fleets, the newer TPU generations (v4/v5/v5p/Ironwood variants) began offering better performance per dollar and better power efficiency compared to some GPU setups.
3. JAX adoption exploded
JAX’s clean API plus XLA compilation provides excellent performance on TPUs. Organizations building foundation models often favor JAX, which naturally increases TPU usage.
4. PyTorch/XLA and tool improvements
PyTorch/XLA and the TPU software stack improved significantly, lowering the barrier for teams that previously relied on PyTorch to move to TPUs without fully rewriting their codebase.
5. TPU & massive scaling
Newer TPU generations offer high sustained throughput and are designed to scale to thousands of chips — a clear advantage when you need to train extremely large transformer models rapidly.
6. Vendor diversification & competitive pricing
Companies do not want to be locked to a single vendor. Google’s competitive TPU pricing, combined with multi-cloud strategies, gave many organizations a compelling reason to try TPUs.
Final thoughts
The AI compute landscape has matured. NVIDIA GPUs remain an excellent, flexible choice, but TPUs have evolved from a Google-only curiosity into a competitive, pragmatic option for organizations building large models. This competition benefits everyone—lower costs, better performance, and more options for architects and researchers.
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