🎓 Distillation is how a big model teaches a small one to think like it.
A large "teacher" model generates outputs or guidance, and a smaller "student" model trains on that to copy its behavior without the same size or cost.
You care because distilled models run cheaper and faster, which matters for on-chain agents, bots, and apps that need low latency at scale.
Example: a trading bot uses a distilled version of a big LLM for real-time signal parsing instead of paying for full model calls every second.
Takeaway: distillation trades a bit of raw power for speed and cost, which is exactly what production systems need.
September 8, 2026 81 1
