GPU vs CPU vs TPU Simulator

Visual comparison simulation of processors for AI & Deep Learning workloads




Processing Race

Watch the dramatic speed difference between processors
Elapsed Time
00:00.000
CPU
--:--
GPU
--:--
TPU
--:--

Processor Architecture

CPU (Central Processing Unit)

┌─────────────────────┐ │ Control Unit │ │ ┌─────┐ ┌─────┐ │ │ │Core1│ │Core2│ │ │ │ ALU │ │ ALU │ │ │ │Cache│ │Cache│ │ │ └─────┘ └─────┘ │ │ ┌─────┐ ┌─────┐ │ │ │Core3│ │Core4│ │ │ │ ALU │ │ ALU │ │ │ │Cache│ │Cache│ │ │ └─────┘ └─────┘ │ │ [ L3 Cache ] │ │ [ DRAM ] │ └─────────────────────┘ 4-16 cores, Complex Logic High Clock Speed, Branch Prediction, Out-of-Order

GPU (Graphics Processing Unit)

┌─────────────────────┐ │ SM SM SM SM SM │ │ ││ ││ ││ ││ ││ │ │ ┌┐ ┌┐ ┌┐ ┌┐ ┌┐ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ ██ ██ ██ ██ ██ │ │ [ HBM / GDDR6 ] │ └─────────────────────┘ 1000s CUDA/Tensor cores SIMT Architecture Massive Parallelism

TPU (Tensor Processing Unit)

┌─────────────────────┐ │ Systolic Array │ │ ┌──┬──┬──┬──┬──┐ │ │ │PE│PE│PE│PE│PE│ │ │ ├──┼──┼──┼──┼──┤ │ │ │PE│PE│PE│PE│PE│ │ │ ├──┼──┼──┼──┼──┤ │ │ │PE│PE│PE│PE│PE│ │ │ ├──┼──┼──┼──┼──┤ │ │ │PE│PE│PE│PE│PE│ │ │ ├──┼──┼──┼──┼──┤ │ │ │PE│PE│PE│PE│PE│ │ │ └──┴──┴──┴──┴──┘ │ │ [ HBM Memory ] │ └─────────────────────┘ 128x128 Systolic Array Matrix Multiply Unit Optimized for Tensor Ops
CPU

Central Processing Unit

8 Cores / 16 Threads @ 5.0 GHz

IDLE
Started
--:--:--
Elapsed
--
Finished
--:--:--

Cores (8 cores - Sequential/Limited Parallel)

Operations/sec 0
Tasks Completed 0 / 0
Throughput 0 GFLOPS
0% Waiting...
GPU

Graphics Processing Unit

4096 CUDA Cores @ 1.8 GHz

IDLE
Started
--:--:--
Elapsed
--
Finished
--:--:--

CUDA Cores (4096 - Massively Parallel)

Operations/sec 0
Tasks Completed 0 / 0
Throughput 0 TFLOPS
0% Waiting...
TPU

Tensor Processing Unit

128x128 Systolic Array @ 940 MHz

IDLE
Started
--:--:--
Elapsed
--
Finished
--:--:--

Processing Elements (Systolic Array)

Operations/sec 0
Tasks Completed 0 / 0
Throughput 0 TFLOPS
0% Waiting...

Time Comparison - The Dramatic Difference

Simulation Log

CPU

Advantages

  • Flexible for all types of computation
  • Excels at sequential & branching tasks
  • Low latency per individual task
  • Most mature software ecosystem
  • Great for preprocessing & data pipelines
  • Easy to debug and optimize

Disadvantages

  • Very limited parallelism (8-64 cores)
  • Slow for matrix/tensor operations
  • Low throughput for batch processing
  • Inefficient for deep learning training
  • High power consumption per FLOP

Best For

Data preprocessing, lightweight model serving, prototyping, general-purpose tasks, logic-heavy workloads

GPU

Advantages

  • Thousands of cores for massive parallelism
  • Most complete AI ecosystem (CUDA, cuDNN)
  • Flexible: training + inference + graphics
  • High memory bandwidth (HBM/GDDR6)
  • Broad framework support (PyTorch, TF)
  • Tensor Cores for mixed-precision

Disadvantages

  • Very high power consumption (300-700W)
  • Expensive (consumer & enterprise)
  • Limited memory vs TPU (24-80GB)
  • CPU-GPU data transfer overhead
  • Less efficient for inference only

Best For

Deep learning training, computer vision, generative AI, research & experimentation, mixed workloads

TPU

Advantages

  • Purpose-built for tensor operations
  • Highest throughput for matrix multiply
  • Best power efficiency per FLOP
  • Systolic array eliminates memory bottleneck
  • Horizontal scaling via TPU pods
  • Integrated with Google Cloud & JAX/TF

Disadvantages

  • Only available via Google Cloud
  • Limited to TensorFlow/JAX
  • Not flexible for non-AI workloads
  • Limited PyTorch support
  • Poor fit for small models/custom ops

Best For

Large model training (LLM, BERT), large-scale inference, TensorFlow/JAX workloads, cost-effective cloud AI

This simulation illustrates conceptual and relative differences. Actual numbers vary based on specific hardware.