Tag: model compression

Learn how compression and quantization enable Large Language Models to run on edge devices, improving privacy, reducing latency, and saving memory.

Explore when to use Edge Inference and Small Language Models (SLMs) over the cloud. Learn about model compression, latency, and on-device AI trade-offs.

Learn how calibration and outlier handling keep quantized LLMs accurate when compressed to 4-bit. Discover which techniques work best for speed, memory, and reliability in real-world deployments.

Recent-posts

How to Write Clear Instructions for LLMs: A Practical Guide to Better AI Output

How to Write Clear Instructions for LLMs: A Practical Guide to Better AI Output

May, 22 2026

Visualization Techniques for Large Language Model Evaluation Results

Visualization Techniques for Large Language Model Evaluation Results

Dec, 24 2025

How to Choose Batch Sizes to Minimize Cost per Token in LLM Serving

How to Choose Batch Sizes to Minimize Cost per Token in LLM Serving

Jan, 24 2026

Curriculum and Data Mixtures: Accelerating LLM Scaling in 2026

Curriculum and Data Mixtures: Accelerating LLM Scaling in 2026

May, 31 2026

Compression-Aware Prompting: Getting the Best from Small LLMs

Compression-Aware Prompting: Getting the Best from Small LLMs

Jun, 7 2026