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SLM Distill Guide

Train small models to beat GPT-4 on your specific task

🔥 Trending: Qwen3-0.6B just beat frontier models on classification tasks at 1% of the cost!

What task do you want to optimize for?

Target model size

💡 Smaller models = faster inference + lower cost. Start small!

How to Use SLM Distill Guide

Create a customized model distillation plan in minutes:

1Describe the specific task you want to optimize for (e.g., text classification, SQL generation)
2Select your target model size (0.6B to 7B)
3Get a complete distillation roadmap with code examples
4Follow the steps to train your custom small model at a fraction of the cost

For teams looking to reduce AI costs while maintaining performance on specific tasks.

Quickly prototype and validate small model approaches before scaling.

Cut AI costs by 99% using distilled models for specific use cases.

Deploy efficient models for task-specific applications without API dependency.

Experiment with distillation techniques using proven frameworks.

Can small models really beat GPT-4?▼

Yes! Qwen3-0.6B recently beat frontier models on classification tasks at 1% of the cost. The key is task-specific training.

What's the cost difference?▼

Small model inference can cost $3/million requests vs $378/million for Gemini. Savings of 99%+.

Do I need GPU hardware?▼

Cloud training services like Google Colab, RunPod, or Lambda Labs work great. No local GPU required.

How long does distillation take?▼

For a 0.6B model, expect 2-6 hours on a single A100. Larger models take longer.