Training LLM models like GPT-4, Claude, or Gemini requires massive computing infrastructure. Two architectures dominate the market: Google's TPUs (Tensor Processing Units) and Nvidia's GPUs. Each solution offers distinct advantages depending on use cases.
This article technically compares these two approaches to help IT decision-makers choose the optimal infrastructure based on their constraints: performance, costs, ecosystem, and flexibility.
1. Architecture: ASIC vs Generic GPU
🔵 Google TPU: Dedicated ASIC
- →ASIC: Application-Specific Integrated Circuit
- →Optimized for dense matrix multiplications
- →Systolic Architecture: optimized data flow
- →High bandwidth memory (HBM)
- →Fast interconnection between TPUs (Ibis)
🟢 Nvidia GPU: Generic Architecture
- →CUDA Architecture: thousands of parallel cores
- →Versatile: graphics, computing, AI
- →Tensor Cores: dedicated AI acceleration
- →NVLink: high-speed GPU interconnection
- →Ecosystem: CUDA, cuDNN, TensorRT
💡 VOID Insight
TPUs are ASICs: their architecture is fixed and optimized for a specific type of computation (matrices). GPUs are programmableand can adapt to different workloads, but with less efficiency for specialized operations.
2. Performance: Real Benchmarks
| Model | FLOPS (FP32) | Memory | Bandwidth | TDP |
|---|---|---|---|---|
| TPU v4 | ~275 TFLOPS | 32 GB HBM | 1.2 TB/s | ~275W |
| TPU v5e | ~197 TFLOPS | 16 GB HBM | ~600 GB/s | ~200W |
| Nvidia A100 | ~156 TFLOPS | 40/80 GB HBM2 | 1.9 TB/s | 250W/400W |
| Nvidia H100 | ~1000 TFLOPS (FP8) | 80 GB HBM3 | 3 TB/s | 700W |
| Nvidia Blackwell | ~2000 TFLOPS (FP4) | 192 GB HBM3e | 8 TB/s | 1200W |
📊 Benchmark Analysis
- TPU v4: Excellent for large-scale TensorFlow training. Superior performance to A100 on optimized workloads.
- H100: Leader for inference and PyTorch training. FP8/FP4 for quantized models.
- Blackwell: New generation for giant LLMs. 2x faster than H100 on certain workloads.
3. Costs and Accessibility
💰 Google Cloud TPU
- TPU v4: ~$8-10/hour per TPU
- TPU v5e: ~$1.50-2/hour per TPU
- Availability: Cloud only (GCP)
- Billing: Per second (minimum 1 minute)
- Discounts: Committed Use Discounts available
💰 Nvidia GPU Cloud
- A100: ~$3-4/hour (AWS, GCP, Azure)
- H100: ~$8-12/hour (varies by region)
- Availability: Multi-cloud + on-premise
- Purchase: ~$10K-40K per GPU (depending on model)
- Flexibility: Spot instances, reservations
⚠️ Hidden Costs
- TPU: High network costs if data outside GCP. Migration to TensorFlow may be necessary.
- GPU: Storage, network, CUDA license costs. On-premise infrastructure maintenance.
4. Use Cases: When to Choose What?
✅ Choose Google TPU if:
- →You use TensorFlow or JAX
- →Predictable workloads (batch training)
- →You're already on Google Cloud Platform
- →Need for maximum energy efficiency
- →Large models requiring fast interconnection
✅ Choose Nvidia GPU if:
- →You use PyTorch or other frameworks
- →Need for flexibility (inference, training, other workloads)
- →Multi-cloud or on-premise required
- →CUDA ecosystem already in place
- →Real-time inference or critical latency
5. Limitations and Constraints
❌ TPU Limitations
- • Cloud only (no on-premise)
- • Optimized for TensorFlow/JAX only
- • Less flexible for mixed workloads
- • Network costs if data outside GCP
- • Specific learning curve
⚠️ GPU Limitations
- • High energy consumption
- • Significant initial purchase costs
- • Complex infrastructure maintenance
- • Limited availability (H100 shortage)
- • Requires CUDA expertise
6. Conclusion: The Strategic Choice
The choice between Google TPUs and Nvidia GPUs largely depends on your existing technical stack and operational constraints.
🎯 VOID Recommendation
- For new TensorFlow/JAX projects: TPU v4/v5e offer the best performance/cost ratio.
- For existing PyTorch ecosystem: H100/Blackwell GPUs remain the reference.
- For multi-cloud/flexibility: Nvidia GPUs to avoid vendor lock-in.
- For mixed workloads: GPUs for versatility, TPUs for pure efficiency.
The rapid evolution of architectures (Blackwell, TPU v6 announced) makes this choice dynamic. A hybrid strategy can be optimal: TPUs for batch training, GPUs for inference and mixed workloads.
Technical Glossary
📚 TensorFlow
Open-source machine learning framework developed by Google. TensorFlow enables building and training deep learning models with an intuitive Python API. It is particularly optimized for Google TPUs and offers excellent production support with TensorFlow Serving.
Use cases: Large-scale model training, production deployment, academic research.
📚 JAX
Python library developed by Google for high-performance scientific computing. JAX combines NumPy with automatic differentiation (autograd) and JIT (Just-In-Time) compilation. It is particularly efficient on TPUs thanks to its XLA (Accelerated Linear Algebra) compilation system.
Use cases: Scientific research, research models, high-performance numerical computing.
📚 PyTorch
Deep learning framework developed by Meta (Facebook). PyTorch is appreciated for its intuitive Pythonic API and dynamic computation mode (eager execution). It has become the reference framework for AI research and works natively on Nvidia GPUs via CUDA.
Use cases: Academic research, rapid prototyping, research models, production (with TorchScript).
📚 CUDA
Compute Unified Device Architecture: parallel computing platform developed by Nvidia. CUDA allows developers to use Nvidia GPUs for general-purpose computing (GPGPU). It includes a compiler, libraries (cuDNN, cuBLAS) and a runtime to execute code on GPU.
Ecosystem: cuDNN (Deep Neural Network), TensorRT (inference optimization), NCCL (multi-GPU communication).
📚 ASIC
Application-Specific Integrated Circuit: integrated circuit designed for a specific application. Unlike generic processors (CPU, GPU), an ASIC is optimized for a precise type of computation, offering maximum performance but limited flexibility. TPUs are ASICs dedicated to neural network operations.
Advantage: Maximum performance and energy efficiency for the targeted use case.
📚 HBM (High Bandwidth Memory)
Type of high-performance memory used in modern TPUs and GPUs. HBM uses a 3D stacked architecture to offer exceptional memory bandwidth (up to 8 TB/s for Blackwell). This technology is essential for feeding high-speed compute cores.
Versions: HBM2 (TPU v4, A100), HBM3 (H100), HBM3e (Blackwell).
📚 TFLOPS
TeraFLOPS: unit of measurement for computing performance representing one trillion floating-point operations per second (10^12 FLOPS). It's a key metric for comparing the raw power of AI processors. Note: actual performance depends on precision type (FP32, FP16, FP8, FP4).
Example: TPU v4 = ~275 TFLOPS (FP32), H100 = ~1000 TFLOPS (FP8).
📚 Systolic Array
Parallel computing architecture used in TPUs. A systolic array is a network of elementary processors organized in a grid, where data "pulses" synchronously through the network. This architecture is optimal for matrix multiplications, the core of neural networks.
Advantage: Maximum efficiency for dense matrix operations, reduced energy consumption.
📚 PyTorch/XLA
PyTorch extension allowing PyTorch code execution on TPU via XLA (Accelerated Linear Algebra). PyTorch/XLA compiles the PyTorch graph to XLA, enabling execution on TPU, but with certain limitations compared to native PyTorch on GPU.
Limitation: Performance generally inferior to TensorFlow/JAX on TPU, some PyTorch operations are not supported.
📚 TDP (Thermal Design Power)
Thermal Design Power: maximum thermal power that a processor can dissipate under normal load. TDP is expressed in watts and indicates typical energy consumption. The higher the TDP, the more powerful the cooling must be.
Example: TPU v4 = ~275W, H100 = 700W, Blackwell = 1200W.
📚 FP32, FP16, FP8, FP4
Floating-point precision formats for AI computing:
- FP32: 32 bits (standard precision, training)
- FP16: 16 bits (mixed precision, 2x faster)
- FP8: 8 bits (quantization, inference, H100)
- FP4: 4 bits (extreme quantization, Blackwell)
Trade-off: Less precision = more performance, but risk of model quality degradation.
Frequently Asked Questions
- Can PyTorch be used on TPU?
- Yes, but with limitations. PyTorch/XLA allows running PyTorch on TPU, but performance is generally inferior to TensorFlow/JAX. For native PyTorch, Nvidia GPUs remain recommended.
- Are TPUs available on-premise?
- No. TPUs are exclusively available via Google Cloud Platform. For on-premise infrastructure, Nvidia GPUs are the only viable option.
- What is the best performance/cost ratio?
- It depends on the workload. For large-scale TensorFlow training, TPU v4 often offers better ROI. For PyTorch inference, H100 GPUs can be more economical thanks to their versatility.
- Can you easily migrate from GPU to TPU?
- Migration possible but non-trivial. If you use TensorFlow, migration is relatively simple. For PyTorch, some code parts need rewriting to optimize for TPU. A code audit is recommended before migration.