Compression 101

Understand the fundamental physics of neural sharding and quantization.

Standard LLMs and Vision models are designed for data centers with 80GB VRAM. Edge devices like the Jetson Nano have 4GB. Compression is the bridge.

Compressed models don't just take less space; they run faster. By reducing the bit-width of weights, we parallelize computations 4x better.

Core Terminologies

Quantization

Reducing the precision of weights (e.g., FP32 to INT8) to save memory.

Pruning

Removing redundant weight neurons that contribute minimally to accuracy.

Distillation

Training a small 'student' model to mimic a large 'teacher' model's behavior.

Sharding

Splitting a model across multiple hardware nodes for distributed edge inference.

Skip the theory and start with our INT8 quantization guide to see instant results.