When 1B Models Learn from Giants: The Complete Architecture of LLM Knowledge Distillation
The economics of Large Language Models present a brutal reality: GPT-4-level performance costs $0.03 per 1K tokens for input and $0.06 for output. Run that at scale—say, 10 million daily queries—and you’re burning $900,000 monthly. But here’s what’s fascinating: researchers have discovered that a 1.3B parameter model, properly distilled from a 175B teacher, can match 95% of its predecessor’s performance on specific tasks while costing 0.1% to run. This isn’t magic. It’s knowledge distillation—a technique that has evolved from Geoffrey Hinton’s 2015 “dark knowledge” paper into a sophisticated ecosystem of methods that compress frontier AI capabilities into models small enough to run on your laptop. ...