The biggest model wins the demo. The smallest model wins the invoice.
My new book, Small Models Win, is a 111-page field guide to the specialist revolution: the published evidence that fine-tuned small models keep beating general giants — and the invoice math that makes it impossible to ignore.
- 111 pages, free — complete book, all 10 chapters plus glossary, in PDF and ePub; free sample PDF (preface + chapters 1–2). Published 2026-09-21, author Yahya Laraki (founder).
- Evidence cited: 13B Orca-2 within a point of GPT-3.5 on reasoning; 7B Meerkat outscoring a 70B rival on MedQA (74.3% vs 70.2%); 7B math specialist hitting 86.81% on GSM8K ahead of GPT-3.5 and LLaMA-2-70B.
- API-bill comparison: ~$3,200/month vs ~$96/month — a 33× gap for the same workload.
- Includes the mechanism (how fine-tuning works, why data beats scale), the economics (invoice math, break-even, when self-hosting wins), and two full chapters on when fine-tuning fails and where scale still wins.
- Playbook chapter: NVIDIA's published recipe for replacing giant models with small ones, plus a migration checklist. Every figure sourced from published papers and official reports; prices are September 2026 snapshots.
Small Models Win: Why Fine-Tuned Small Models Outperform General Models started as a suspicion and became a thesis I could defend with citations. Across medicine, law, finance, code, math, and speech, the pattern repeats: a small model, fine-tuned on the right data, matches or beats a general model many times its size — at a fraction of the cost.
A few of the results that made it into the book:
- A 13B model (Orca-2) scoring within a point of GPT-3.5 on reasoning benchmarks.
- A 7B medical model (Meerkat) outscoring a 70B rival on MedQA: 74.3% vs 70.2%.
- A 7B math specialist hitting 86.81% on GSM8K, ahead of GPT-3.5 and LLaMA-2-70B.
- An API bill comparison with a 33× gap: ~$3,200/month vs ~$96/month for the same workload.
What's inside#
- The evidence, domain by domain — medicine, law, finance, code, math, reasoning, language, retrieval, speech.
- The mechanism — how fine-tuning actually works, and why data beats scale.
- The economics — the full invoice math, break-even analysis, and when self-hosting wins.
- The honesty — two full chapters on when fine-tuning fails and where scale still wins. The thesis is conditional, not a slogan.
- The playbook — NVIDIA's published recipe for replacing giant models with small ones, plus a migration checklist.
Every figure is sourced from published papers and official reports, with per-chapter sources. Prices are September 2026 snapshots and labeled as such.
The book is free#
Small Models Win is free — the complete book, all 10 chapters plus the glossary, in both formats: PDF and ePub. The details page has the chapter breakdown, and the free sample PDF (preface plus chapters 1–2) is there if you want a taste first.
Frequently asked questions
What is ‘Small Models Win’ about?
A 111-page field guide by Yahya Laraki making the cited, conditional case that fine-tuned small models keep beating general giants — across medicine, law, finance, code, math, and speech — at a fraction of the cost. It includes two full chapters on when fine-tuning fails and where scale still wins, plus a migration checklist based on NVIDIA's published recipe.
What evidence does the book cite?
A 13B Orca-2 scoring within a point of GPT-3.5 on reasoning benchmarks, a 7B medical model (Meerkat) outscoring a 70B rival on MedQA (74.3% vs 70.2%), a 7B math specialist hitting 86.81% on GSM8K ahead of GPT-3.5 and LLaMA-2-70B, and an API-bill comparison showing a 33× gap (~$3,200/month vs ~$96/month).
Is the book really free?
Yes — the complete book, all 10 chapters plus the glossary, in both PDF and ePub formats, with a free sample PDF (preface + chapters 1–2). Every figure is sourced from published papers and official reports, with prices as September 2026 snapshots.