ai-accelerators

What problem does it solve?

This Skill addresses the performance bottlenecks in machine learning by detailing and optimizing the use of specialized hardware like GPUs, TPUs, and edge accelerators.

Core Features & Use Cases

• Hardware Overview: Understand the landscape of AI accelerators (GPUs, TPUs, NPUs, FPGAs, ASICs, Edge).
• Optimization Strategies: Learn techniques for GPU (CUDA, Tensor Cores, Multi-GPU) and TPU (JAX, PyTorch/XLA) optimization.
• Edge Deployment: Explore optimization for edge devices using TensorRT, DeepStream, and TFLite.
• Hardware-Aware Tuning: Implement auto-tuning with TVM and hardware selection guidance.
• Use Case: Optimize a deep learning model for faster inference on an NVIDIA Jetson device using TensorRT, or select the most cost-effective GPU cluster for training a large language model based on budget and performance requirements.

Quick Start

Use the ai-accelerators skill to find optimization strategies for running PyTorch models on NVIDIA GPUs.