Event details
Are you paying for more model than the task requires?
For many enterprise production tasks, frontier models bring more capability—and more cost—than the job actually requires. The real work is often much more refined: classify, extract, judge, call a tool, or reliably follow a defined agent loop.
Model distillation offers a different path when you only need a narrow slice of a model’s broader capabilities. It captures how the larger model behaves in production, uses that behavior to train a smaller student model, and evaluates whether the smaller model can meet your requirements at a potentially much lower cost.
Join CoreWeave for a 40-minute deep dive into model distillation and post-training optimization. You’ll learn where distillation, Serverless SFT, and Serverless RL fit, how the approaches differ, and how to decide which path makes sense for your workload and team.
In this webinar, we’ll cover and demonstrate:
- How to identify production tasks that may not need a frontier-model capability
- How model distillation turns real production usage into training data for a smaller student model
- Where distillation, supervised fine-tuning, and reinforcement learning differ—and when each makes the most sense
- How to evaluate quality, cost, timeline, and team requirements before choosing a post-training path
- How a guided model-distillation workflow moves from Weave traces and dataset curation through Serverless SFT, evaluation, and deployment guidance
Learn how to turn real usage into a smaller, better-fit model without needing a dedicated research organization.
