🧠 What is a TPU? Google's Secret Weapon Explained

Last Updated: January 23, 2024 | 10 min read | Jump to Pricing

Key Takeaways:

What Exactly is a TPU?

A Tensor Processing Unit (TPU) is Google's custom-developed Application-Specific Integrated Circuit (ASIC) designed specifically for neural network machine learning. Unlike GPUs which are general-purpose parallel processors, TPUs are optimized exclusively for the matrix multiplication operations that dominate deep learning workloads.

Think of it this way: If GPUs are Swiss Army knives (versatile but not specialized), TPUs are surgical scalpels—incredibly efficient at one specific task.

🏗️ TPU Architecture: Built Different

TPU v4 Architecture

┌─────────────────────────────────────┐
│          TPU v4 Chip                │
│  ┌─────────────┐  ┌─────────────┐  │
│  │  Matrix     │  │  Matrix     │  │
│  │  Multiply   │  │  Multiply   │  │
│  │  Unit (MXU) │  │  Unit (MXU) │  │
│  └─────────────┘  └─────────────┘  │
│         ↓                ↓          │
│  ┌──────────────────────────────┐  │
│  │     Vector Processing Unit    │  │
│  └──────────────────────────────┘  │
│         ↓                          │
│  ┌──────────────────────────────┐  │
│  │    32GB HBM Memory           │  │
│  └──────────────────────────────┘  │
└─────────────────────────────────────┘

TPUs use systolic arrays for maximum efficiency in matrix operations

Key Architectural Differences:

Feature GPU (H100) TPU v4 Winner
Design Philosophy General parallel compute Tensor operations only Depends on use case
Programming Model CUDA, various frameworks TensorFlow, JAX primarily GPU (flexibility)
Memory 80GB HBM3 32GB HBM per chip GPU (capacity)
Power Consumption 700W 200W TPU (3.5x efficient)
Peak FLOPS 989 TFLOPS (FP16) 275 TFLOPS (bfloat16) GPU (raw power)
Cost per FLOP Higher Lower TPU (efficiency)
Availability Multiple cloud providers Google Cloud only GPU (accessibility)

📊 TPU Generations Evolution

TPU v1 (2016) - The Beginning

Performance: 92 TOPS
Memory: 8GB DDR3
Purpose: Inference only
Power: 40W

Used internally for Google Search, Photos, and Translate

TPU v2 (2017) - Training Capable

Performance: 180 TFLOPS
Memory: 64GB HBM
Purpose: Training + Inference
Power: 280W

First TPU available on Google Cloud

TPU v3 (2018) - Scale Up

Performance: 420 TFLOPS
Memory: 128GB HBM
Cooling: Liquid cooled
Power: 450W

2.3x faster than v2, pods scale to 2048 chips

TPU v4 (2021) - Current Generation

Performance: 275 TFLOPS
Memory: 32GB HBM
Interconnect: ICI 2.0
Power: 200W

2.7x performance/watt improvement, optical interconnects

💪 TPU vs GPU: Real-World Performance

Workload TPU v4 Pod-32 8x A100 Cluster Winner
BERT-Large Training 2.3 hours 3.8 hours TPU (1.65x faster)
ResNet-50 Training 28 minutes 35 minutes TPU (1.25x faster)
GPT-3 13B Fine-tune 4.5 hours 5.2 hours TPU (1.15x faster)
Stable Diffusion Not optimized 12 img/sec GPU (compatibility)
Custom CUDA Kernels Not supported Full support GPU (flexibility)
Power Efficiency 1.375 TFLOPS/W 0.78 TFLOPS/W TPU (1.76x efficient)

✅ When to Use TPUs

Perfect For:

  • ✓ Large-scale TensorFlow/JAX training
  • ✓ Transformer models (BERT, GPT)
  • ✓ Batch inference at scale
  • ✓ Research with free Colab TPUs
  • ✓ When power costs matter
  • ✓ Google Cloud native workloads

Avoid For:

  • ✗ PyTorch-first workflows
  • ✗ Custom CUDA kernels
  • ✗ Small-scale experiments
  • ✗ Multi-cloud deployments
  • ✗ Gaming/graphics workloads
  • ✗ Variable precision needs

🚀 Getting Started with TPUs

# Quick start with TPUs in TensorFlow
import tensorflow as tf

# Connect to TPU
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)

# Create distribution strategy
strategy = tf.distribute.TPUStrategy(resolver)

# Build model within strategy scope
with strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dense(10, activation='softmax')
    ])

    model.compile(
        optimizer='adam',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )

# Training runs on TPU automatically
model.fit(train_dataset, epochs=10)

💰 TPU Pricing on Google Cloud

TPU v4 Pricing (January 2024)

Configuration On-Demand Preemptible 1-Year Commit
TPU v4-8 (single host) $3.22/hour $0.97/hour $2.25/hour
TPU v4 Pod-32 $12.88/hour $3.86/hour $9.02/hour
TPU v4 Pod-128 $51.52/hour $15.46/hour $36.06/hour

Note: TPU v5e (efficiency-optimized) available at 50% lower cost for inference

🔬 Real Use Cases

Google Search & Ranking

TPUs power Google's search ranking models, processing billions of queries daily with sub-millisecond latency. The energy efficiency allows Google to scale sustainably.

AlphaFold Protein Folding

DeepMind used TPU v4 pods to train AlphaFold, reducing training time from months to weeks. The massive parallelism of TPU pods was crucial for this breakthrough.

Large Language Models

Google's PaLM and Gemini models are trained exclusively on TPU v4 pods, leveraging the 4096-chip configurations for unprecedented scale.

🎯 The Verdict: TPU or GPU?

Choose TPUs if:

Stick with GPUs if:

Bottom Line: TPUs are incredibly powerful for specific workloads but lack the flexibility of GPUs. For most teams, GPUs remain the safer choice unless you're fully committed to the Google Cloud ecosystem and TensorFlow/JAX frameworks.

Calculate Your GPU/TPU Costs →