ONNX

A Practical Guide to Efficient AI: Shelby Heinecke

Nov 18, 2024 · 17:45

Shelby Heinecke, who leads an AI research team at Salesforce, presents five orthogonal dimensions for making AI models efficient: efficient architecture selection, pre-training, fine-tuning, inference, and prompting. She highlights the power of small models like Phi-3 (3.8B parameters outperforming a 7B model), mobile LLM (350M parameters on par with 7B after fine-tuning), and Octopus (2B fine-tuned Gemma exceeding GPT-4 on Android tasks). For efficient inference, she explains post-training quantization, showing 4-bit quantization nearly halves memory usage without performance loss (e.g., LLaMA models), but warns 3-bit can degrade quality. She recommends frameworks like LLaMA CBP and ONNX Runtime for quantization and introduces her team's open-source Mobile AI Bench for evaluating quantized models, including an iOS app to measure latency and battery drain. The central claim is that deploying AI in constrained environments—cloud, on-prem, or edge—demands efficiency, and these practical techniques bridge the gap from demo to production.

Unlocking Developer Productivity across CPU and GPU with MAX: Chris Lattner

Jul 25, 2024 · 18:33

Chris Lattner, CEO of Modular, presents MAX, a unified AI framework that accelerates Gen AI inference by combining CPU and GPU programming into a single Pythonic model, and Mojo, a new programming language that extends Python to systems programming with 100–1000x speedups. Lattner argues that current fragmentation across PyTorch, ONNX, TensorRT, and hardware-specific libraries slows innovation, and MAX replaces the entire stack—including cuDNN and Intel MKL—with a consistent, compiler-driven approach. He demonstrates that MAX's Int4/Int6 quantization achieves 5x faster performance than llama.cpp on cloud CPUs, and that its GPU matrix multiplication beats NVIDIA's cuBLAS by up to 30%. Mojo enables developers to write for loops and tokenizers (e.g., for LLaMA 3) in Python-like syntax without dropping to C++ or Rust. MAX is free and available now for CPU inference; GPU support launches in September with early access via Discord.