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Skills2026-06-07 · source-backed

Distill instead of fine-tune when the goal is inference cost, not new behavior.

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LoRA/QLoRA adapts behavior but leaves a same-size, same-cost model. If you want cheaper inference, distillation gives you a permanently smaller one. A distilled Llama 3.1 8B trained on a 70B teacher captures 90 to 95% of quality at ~10% of inference cost. The hybrid move: distill for size, then rank-stabilized LoRA on the small model for domain fit. Source: DEV Community

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2026-06-07-distill-instead-of-fine-tune-when-the-goal-is-inference-cost-not-new-behavior
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