Overview
Tensor Processing Units (TPUs), developed by Google, are specialized ASICs optimized for tensor operations central to machine learning, particularly large language models (LLMs). Graphics Processing Units (GPUs), led by NVIDIA, are more general-purpose parallel processors with broad AI support. As of November 2025, TPUs excel in efficiency for specific LLM workloads but cannot fully replace GPUs due to ecosystem limitations, flexibility, and accessibility. Below, I'll break this down for training and inference, supported by recent benchmarks and analyses.
For Training LLMs
TPUs can handle LLM training effectively—Google trains models like Gemini and PaLM on massive TPU pods—but they don't fully replace GPUs for most users or scenarios.
- Performance and Efficiency: Google's TPU v5p leads in training throughput for dense transformers, often matching or exceeding NVIDIA's H200 GPUs in tokens/second per dollar on TensorFlow/JAX workflows. For example, TPU v5p pods scale to 8,960 chips for trillion-parameter models, with up to 4–10x cost-efficiency over equivalent GPU clusters for 70B+ parameter LLMs. Upcoming TPU v6 (Trillium) promises 2.8x performance gains and 2.1x better performance per watt. However, NVIDIA's Blackwell B200 GPUs set MLPerf records with 3x speedups over H100s in PyTorch environments, making them faster for heterogeneous or mixed-precision training.
- Limitations: TPUs require framework optimization (e.g., TensorFlow or JAX), limiting portability. GPUs dominate with 80% market share, supporting PyTorch, DeepSpeed, and multi-cloud setups seamlessly. Training non-standard architectures or fine-tuning open-source LLMs (e.g., LLaMA) is often easier and faster on GPUs.
| Aspect | TPUs (e.g., v5p/v6) | GPUs (e.g., H200/B200) |
|---|---|---|
| Best For | Large-scale, uniform tensor ops (e.g., GPT-like) | Versatile, distributed training (PyTorch) |
| Scalability | Pods up to 8K+ chips; energy-efficient | NVLink clusters; broad multi-vendor support |
| Cost (per token) | 4–10x cheaper in Google Cloud | Higher but more accessible on-prem/cloud |
| Market Share | ~5–6% of AI deployments | ~80% |
In summary, TPUs replace GPUs for Google-centric, high-volume training but not universally—most enterprises stick with GPUs for flexibility.
For Inference (Deployment)
TPUs shine here, especially for cost-sensitive, low-latency serving, but GPUs remain the default for production due to mature tools.
- Performance and Efficiency: TPU v5e delivers 2.5x throughput per dollar and 1.7x speedup over v4 for LLMs, with Ironwood (v7, GA in Nov 2025) optimized for real-time MoE models and agents—achieving sub-1s time-to-first-token (TTFT) for LLaMA 70B at low concurrency. Disaggregated TPU serving boosts prefill/decode by 3–7x on Trillium. GPUs like H100/H200 handle high-concurrency (50+ users) better, with ~140 tokens/s via TensorRT-LLM, but at higher energy costs.
- Limitations: Inference frameworks like vLLM and Hugging Face TGI are GPU-native, with limited TPU support outside Google Cloud—leading to vendor lock-in. TPUs excel in batch inference but struggle with dynamic, variable-length prompts common in chat apps.
| Aspect | TPUs (e.g., v5e/Ironwood) | GPUs (e.g., H100/H200) |
|---|---|---|
| Best For | Low-latency, high-volume (e.g., search APIs) | High-concurrency, dynamic serving (e.g., chatbots) |
| Latency | TTFT ~0.3–0.76s for 70B models | Sustained 140+ tokens/s; flexible quantization |
| Cost | Up to 2.5x cheaper for scale | Broader availability; optimized runtimes |
| Ecosystem | TensorFlow/JAX; JetStream/vLLM on GCP | vLLM/TensorRT; multi-cloud/on-prem |
TPUs can replace GPUs for inference in optimized Google environments (e.g., Osmos scales cost-efficiently on Trillium), but GPUs' versatility prevents full replacement.
Key Barriers to Full Replacement
- Ecosystem Lock-in: TPUs are Google Cloud-bound, with poor support for non-TensorFlow frameworks—PyTorch users (majority) prefer GPUs.
- Availability and Cost: GPUs are ubiquitous (AWS, Azure, on-prem); TPUs are cloud-only and pricier for small-scale.
- Versatility: GPUs handle diverse AI tasks beyond LLMs; TPUs are tensor specialists, hitting walls on memory-bound or irregular workloads.
- Adoption Trends: By 2025, GPUs hold ~80% share; TPUs ~5–6%, growing but niche.
Conclusion
No, TPUs cannot fully replace GPUs for LLM training and inference in 2025—they're complementary, with TPUs winning on efficiency in Google-optimized pipelines and GPUs on flexibility and ubiquity. Choose based on your stack: TPUs for cost-scale in TensorFlow, GPUs for everything else. Hybrid setups (e.g., train on TPUs, infer on GPUs) are increasingly common for balanced performance.
