Skip to main contentSkip to user menuSkip to navigation
System Designer
Back to Reference

ML Frameworks Comparison

Not Started
Loading...

ML Frameworks Comparison

Complete comparison of PyTorch, TensorFlow, and JAX for production ML systems

Framework Selection Calculator

Feature Comparison Matrix

FeaturePyTorchTensorFlowJAX
Learning Curve🟢 Easy🟡 Moderate🔴 Steep
Dynamic Graphs🟢 Native🟢 Eager Mode🟡 Functional
Production Deployment🟡 TorchServe🟢 TF Serving🔴 Limited
Mobile/Edge Support🟡 PyTorch Mobile🟢 TF Lite🔴 None
Distributed Training🟢 FSDP/DDP🟢 Strategy API🟢 pmap/xmap
Compilation/XLA🟡 torch.compile🟢 XLA🟢 JIT
Ecosystem Size🟢 Huge🟢 Huge🟡 Growing
Research Community🟢 Dominant🟡 Strong🟢 Elite
Industry Adoption🟢 Meta, Tesla🟢 Google, Uber🟡 DeepMind

Performance Benchmarks

PyTorch

94.2%
ResNet-50 Training
V100, Baseline

TensorFlow

98.7%
ResNet-50 Training
V100, XLA Enabled

JAX

99.1%
ResNet-50 Training
V100, JIT Compiled
* Benchmarks based on MLPerf Training v2.1 results. Performance varies by model architecture and hardware configuration.

Use Case Recommendations

Choose PyTorch For:

  • ✅ Research and experimentation
  • ✅ Rapid prototyping
  • ✅ Computer vision projects
  • ✅ Dynamic neural networks
  • ✅ Academic environments
  • ✅ When you need debugging flexibility

Choose TensorFlow For:

  • ✅ Production deployment
  • ✅ Mobile and edge applications
  • ✅ Large-scale distributed training
  • ✅ Enterprise environments
  • ✅ When you need robust serving
  • ✅ Multi-platform deployment

Choose JAX For:

  • ✅ Scientific computing
  • ✅ High-performance research
  • ✅ When you need pure functions
  • ✅ Functional programming preference
  • ✅ Advanced optimization needs
  • ✅ NumPy compatibility

Migration Considerations

PyTorch → TensorFlow

Focus on static graph representation, model serving infrastructure, and TensorFlow ecosystem tools. Budget 2-4 months for complete migration of a production system.

TensorFlow → PyTorch

Embrace dynamic computation, refactor training loops, and rebuild serving infrastructure. Consider TorchServe for deployment. Timeline: 3-6 months for enterprise systems.

Any Framework → JAX

Requires functional programming mindset, immutable operations, and custom training loops. Best for teams with strong ML engineering background. Timeline: 4-8 months.

No quiz questions available
Quiz ID "ml-frameworks-comparison" not found