Model Compression

The economic reality of enterprise AI is that the models that achieve the best benchmark scores are also the most expensive to run. A 70-billion-parameter model may be accurate, but at $0.015 per 1,000 tokens and millions of queries per day, the cost becomes prohibitive. Model compression techniques address this by modifying the model's internal representation or architecture to reduce memory footprint and computation, enabling deployment on cheaper hardware or at higher throughput on existing infrastructure.

The compression toolkit has three primary branches. **Quantization** reduces the numerical precision of model weights from 32-bit or 16-bit floats to 8-bit integers or even 4-bit representations, slashing memory requirements with minimal accuracy loss on most tasks. **Pruning** removes weights or entire attention heads that contribute little to model outputs, creating a sparser network with fewer active parameters at inference time. **Knowledge distillation** trains a smaller "student" model to mimic the behavior of a larger "teacher" model, producing a purpose-built compact model that often outperforms a general compressed model of the same size on the target task.

These techniques are not mutually exclusive — production deployments frequently stack them. A model might be distilled to a smaller architecture, then quantized for the target hardware, then pruned further for a specific inference serving configuration. The result is a deployment-optimized model artifact tuned for the specific cost-performance tradeoff your enterprise requires. The key enterprise skill is measuring the performance degradation at each compression stage against your specific task distribution, not general benchmarks.