What Is SLM Distillation? A Practical Guide for AI Engineers
By NeoSmith AI Research Team. March 16, 2026. 14 min read.
SLM distillation is the process of transferring knowledge from a large language model into a smaller language model purpose-built for a specific task or workflow. The student model typically has fewer than 1 billion parameters. The goal is to create a specialist that does one thing exceptionally well, at a fraction of the cost and latency.
Key Takeaways
- SLM distillation produces models that run 10 to 100x cheaper per inference call
- Quality and domain relevance matter far more than dataset quantity
- Reward design matters more than model scale for specialized tasks