EigenWeights: Haciendo que Modelos Grandes Quepan en Cualquier Lugar

The Model Size Problem

State-of-the-art AI models continue to grow. A 70B parameter model requires 140GB just for weights in float16—far exceeding the memory capacity of most consumer and edge devices.

Existing compression techniques like quantization offer 2-4x reduction but often sacrifice quality or require expensive retraining. We needed a different approach.

Our Approach

EigenWeights exploits the structural redundancy present in neural network weight matrices. Rather than treating weights as arbitrary numbers to be quantized, we identify and preserve the mathematically essential components.

The visualization above illustrates how EigenWeights transforms dense, fully-connected layers into sparse, structured representations while preserving network behavior.

Technical Overview

Structural Decomposition

We decompose weight matrices into components ordered by their contribution to model behavior. This allows precise control over the compression-quality tradeoff.

Adaptive Precision

Different layers and components receive different treatment based on their sensitivity. Critical pathways retain full precision while redundant connections are aggressively compressed.

Hardware-Aware Optimization

Our compressed format is designed for efficient execution on target hardware, often achieving speedups beyond what raw size reduction would suggest.

Benchmark Results

We evaluated EigenWeights across standard benchmarks, comparing compressed models against their full-precision counterparts:

Across all benchmarks, compressed models retain >98% of baseline performance while using 8x less memory.

Detailed Comparison

Deployment Scenarios

EigenWeights enables new deployment possibilities: Run 70B models on single consumer GPUs, deploy production models on edge devices, reduce cloud inference costs by 8x, and enable on-device AI without cloud dependencies.