8bit.tr Journal
Model Compression and Distillation: Smaller Models, Real Gains
A practical guide to compressing LLMs with quantization, pruning, and distillation while preserving quality.
Compression Is an Engineering Choice
Smaller models reduce latency and cost, but the trade-off is quality.
A good compression strategy preserves the tasks that matter most to your product.
Quantization as the First Lever
Quantization lowers precision to reduce memory and speed up inference.
Modern quantization techniques keep quality loss minimal for many tasks.
Pruning and Structured Sparsity
Pruning removes redundant weights. Structured pruning is easier to deploy because it aligns with hardware.
Unstructured sparsity can be harder to exploit without custom kernels.
Distillation for Task-Focused Models
Distillation trains a smaller model to mimic a larger one on specific tasks.
It is especially effective when you have a narrow, well-defined use case.
Evaluation and Regression Safety
Always evaluate compressed models against a baseline on real tasks.
Regression testing prevents silent quality drops that users notice immediately.
Deployment Checklist
Roll out compression in stages. Start with a low-risk route and compare latency, cost, and user satisfaction before full rollout. This keeps quality surprises contained.
Keep a canary model with the original weights. If quality drops, you can immediately fall back without waiting for a retrain.
Document which tasks are most sensitive to compression. Use those tasks as release gates for every update.
Communicate expected quality changes to stakeholders so support teams are ready for edge cases.
Schedule post-release audits to ensure quality remains stable after real traffic shifts.
Update documentation and model cards so downstream teams understand the new model characteristics.
Track support tickets after rollout to catch regressions that metrics miss.
Set a minimum quality threshold for rollback and enforce it consistently.
Review quality monthly to ensure compression benefits persist as usage evolves.
Keep a shadow evaluation on uncompressed models for critical workflows.
Log per-task quality deltas so you can see which use cases benefit or regress.
FAQ: Compression
Is distillation better than quantization? They solve different problems and are often combined.
How much can I compress? It depends on task tolerance, but 2x to 4x is common.
What is the biggest risk? Over-optimizing cost and losing user trust.
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