TPU在LLM训练和推理中全面替代GPU吗？

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.