Tag: LLM quantization

Running LLMs on Edge Devices: Compression and Quantization Guide

Running LLMs on Edge Devices: Compression and Quantization Guide

Learn how to run large language models on edge devices using compression and quantization. Explore PTQ, QAT, GPTVQ, and tools like AIMET for efficient local AI deployment.

Read More
Post-Training Quantization for Large Language Models: 8-Bit and 4-Bit Methods Explained

Post-Training Quantization for Large Language Models: 8-Bit and 4-Bit Methods Explained

Post-training quantization cuts LLM memory use and speeds up inference by 2-3x without retraining. Learn how 8-bit and 4-bit methods like SmoothQuant, AWQ, and GPTQ make it possible-and what you need to know to use them.

Read More
Quantization-Aware Training for LLMs: How to Keep Accuracy While Shrinking Model Size

Quantization-Aware Training for LLMs: How to Keep Accuracy While Shrinking Model Size

Quantization-aware training lets you shrink large language models to 4-bit without losing accuracy. Learn how it works, why it beats traditional methods, and how to use it in 2026.

Read More