How are GPUs and TPUs different in training large transformer models? Top GPUs and TPUs with benchmarks

Both GPU and TPU Playing a crucial role in accelerating the training of large transformer models, but their core architecture, performance profiles, and ecosystem compatibility create significant differences in use cases, speed, and flexibility.

Architecture and Hardware Basics

TPU is Customized ASIC (Application-specific integrated circuits) designed by Google and designed specifically for efficient matrix operations required by large neural networks. Their design focuses on vector processing, matrix multiplication units and shrink arrays to achieve special throughput on the transformer layer and in-depth integration with TensorFlow and Jax.

The GPU is dominated by NVIDIA’s CUDA-capable chips, using thousands of general-purpose parallel cores with dedicated tensor units, high-bandwidth memory, and complex memory management systems. Although originally designed for graphics, modern GPUs now provide optimized support for large-scale ML tasks and a wider model architecture.

Transformer training performance

• TPU Large-scale batch processing and large-scale GPUs directly compatible with their architecture, including most tensor-based LLM and transformer networks. For example, Google’s V4/V5P TPU is up to 2.8 times more in training models like Palm and Gemini than some previous TPUs, and consistently eliminates GPUs, such as the A100, such as the A100, for these workloads.
• GPU Provides powerful performance for a variety of models, especially those that use dynamic shapes, custom layers, or models other than frameworks. gpus excel is in smaller batch sizes, unconventional model topology and solutions that require flexible debugging, custom kernel development or non-standard operation.

Software ecosystem and framework support

• TPU It is closely integrated with Google’s AI ecosystem and mainly supports Tensorflow and Jax. Pytorch support is available, but is not very mature, and is less adopted in production workloads.
• GPU Supports almost every major AI framework, including Pytorch, Tensorflow, Jax and MXNET, supported by mature toolchains such as Cuda, Cudnn and Rocm.

Scalability and deployment options

• TPU Seamless scaling with Google Cloud allows training of super-large models on POD-scale infrastructure with thousands of interconnect chips to maximize throughput and minimal latency for distributed settings.
• GPU Provides extensive deployment flexibility on cloud, on-premises and offers multi-vendor availability (AWS, Azure, Google Cloud, private hardware) and provides extensive support for containerized ML, orchestration and distributed training frameworks (e.g., DeepSpeed, DeepSpeed, Megatron-LM).

Energy efficiency and cost

• TPU Well-designed data center efficiency, usually per watt, total project costs for compatible workflows are low.
• GPU catching up with higher efficiency in newer generations, but often requires higher total power consumption and cost of oversized production runs compared to an optimized TPU.

Use cases and limitations

• TPU Use TensorFlow to train a very large LLM (Gemini, Palm) in the Google Cloud ecosystem. They struggle with models that require dynamic shapes, custom actions, or advanced debugging.
• GPU Preferred for experiments, prototyping, training/fine-tuning, and deployments that require local or diverse cloud options that require. Most commercial and open source LLMs (GPT-4, Llama, Claude) run on high-end Nvidia GPUs.

Summary comparison table

TPU and GPU are designed for different priorities: TPU uses Google’s stack to maximize the throughput and efficiency of the transformer model, while GPU provides ML practitioners and enterprise teams with universal flexibility, mature software support, and a wide range of hardware options. For training large transformer models, select the accelerator that extends with model framework, workflow requirements, debug and deployment requirements, and ambitions for the project.

According to MLPERF and independent deep learning infrastructure reviews, Google’s TPU V5P and NVIDIA’s Blackwell (B200) and H200 GPUs currently implement the best 2025 training benchmarks for large transformer models.

Top TPU models and benchmarks

• Google TPU V5P: Provides market-leading performance for training LLM and dense transformer networks. The TPU V5P provides substantial improvements over previous TPU versions, allowing large-scale proportions (up to thousands of chips) within Google Cloud Pods and supports models up to 500B parameters. For TensorFlow/JAX-based workloads, TPU V5P has high throughput, cost-effective training and class-leading efficiency.
• Google TPU Ironwood (for inference): Optimized for inferred transformer models to achieve first-class speed and minimum energy consumption for production scale deployment.
• Google TPU V5E: Provides strong price performance, especially for large models in budget, up to 70B+ parameters. For large LLMs, TPU V5E can be more cost-effective than a large GPU cluster.

Top GPU models and benchmarks

• Nvidia Blackwell B200: The new Blackwell architecture (GB200 NVL72 and B200) shows record-breaking throughput in the MLPerf V5.0 benchmark, achieving 3.4x per capita performance compared to the MLPERF V5.0 benchmark for models like the Mixtral 8x7b. System-level acceleration with NVLINK domains is 30 times larger than the older generation.
• NVIDIA H200 Tensor Core GPU: Highly efficient in LLM training, with greater bandwidth (10TB/s), improved FP8/BF16 performance, and H100 for fine-tuning of transformer workloads. In an enterprise cloud environment, the Blackwell B200 outperforms the Blackwell B200, but remains the most widely supported and available option.
• NVIDIA RTX 5090 (Blackwell 2.0): Newly launched in 2025, delivering up to 104.8 Tflops unit performance and 680 fifth-generation tensor cores. It is ideal for research labs and medium-scale production, especially when performance and local deployment are major issues.

MLPERF and the highlights in the real world

• TPU V5P and B200 prove that the training throughput and efficiency of a large number of LLMs are the fastest, the B200 provides 3× speeds in the previous generation, and MLPERF confirms the recording tokens per second for multi-GPU NVLINK clusters.
• TPU Pods retain the advantages of every price, energy efficiency and scalability of Google’s cloud-centric Tensorflow/JAX workflow, while the Blackwell B200 occupies MLPERF for Pytorch and heterogeneous environments.

These models represent the industry standard for large transformer training in 2025, with both TPU and GPU providing state-of-the-art performance, scalability and cost-effectiveness, based on the framework and ecosystem.

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