ai-accelerators - AI Accelerators Optimization
Hardware acceleration strategies for ML workloads on GPUs, TPUs, and edge devices
Tags
Updated: 2026-06-30Capabilities
Typical Inputs
Typical Outputs
What this skill does
- optimize CUDA memory
- utilize tensor cores
- configure multi-GPU setup
- run TPU training
- deploy edge models
- convert TFLite models
- auto-tune hardware
- benchmark performance
Inputs
- GPU/TPU hardware
- ML models
- training data
- ONNX model files
- TFLite model files
Outputs
- optimized model
- TensorRT engine
- Edge TPU model
- performance metrics
- hardware recommendations
Requirements
- AI accelerator hardware
- CUDA/ROCm runtime
- PyTorch or JAX
- TensorRT or XLA
- Edge device support