What is Model Distillation?
Model distillation compresses capabilities from a teacher model into a smaller student model. It preserves task performance while reducing latency, memory, and cost, which is useful for edge/on-prem or high-volume workloads.
How does Model Distillation work?
Train the student to mimic teacher logits or responses over curated datasets. Combine with task-specific fine-tuning and evaluation to ensure quality holds under constraints.
When should you use it? (Typical use cases)
- On-device or on-prem assistants with strict latency.
- High-volume classification/extraction workloads.
- Cost-sensitive chat and summarization services.
- Privacy-constrained environments where small models are preferred.
Benefits and risks
Benefits
- Lower inference cost
- Smaller footprint
- Faster response times
Common pitfalls/risks
- Loss of reasoning depth
- Overfitting to teacher quirks
Antire and Model Distillation
We evaluate compression strategies (distillation, pruning, quantization) against your KPIs and compliance needs.
Services
Data platforms and applied AI
Tailored AI & ML
Cloud-native business applications
Fast Track Agentic Value Sprint
Related words: Fine-tuning, Open-weight models, Compression, Edge inference