Quantization is a technique that helps run Large Language Models (LLMs) by making them smaller, faster, and resource-friendly—like exchanging a heavy suitcase for a small backpack! 🎒#machine_learning #LLMs A thread: 🧵(1/6)

Quantization enables AI to be used in daily life: Speed: By reducing precision, it accelerates calculations, improving performance. Size: Models reduce in size, saving storage and memory. Accessibility: Makes AI compatible with low-power devices, such as smartphones! #LLMs (2/6)

Quantization powers real-world AI by boosting speed for real-time LLM inference, cutting costs with smaller models for cheaper cloud/edge deployment, and saving energy, extending battery life in devices like smartwatches. #AI (3/6)

Original models use floating-point numbers, demanding more memory, power, and energy. Quantized models use integers, making them smaller, faster, and more efficient—ideal for real-world use, especially on edge devices like smartphones and IoT! #Accurate (4/6)

Tools like TensorFlow Lite (for edge/mobile devices) and ONNX Runtime (for cross-platform AI optimization) make quantization simple, enabling scalable, efficient, and universal AI deployment. #TensorFlowLite #tools (5/6)

Quantization = speed, optimization, and scalability! 🚀 It is the key to real-world AI innovations, enabling models to function effectively without compromising effectiveness. In your opinion, what impact does quantization have in AI? Let’s discuss!✨ (6/6)