LinkedIn publication text
From Zero to Weights: how a frontier AI is born
Today, October 4, 2026, I am publishing a special edition of the AI World BRIEFING: a 19-slide infographic covering the complete training lifecycle of an LLM, from the initial compute decisions to weight release.
What the research makes clear: the greatest effort lies in data and infrastructure, not in architecture. The Llama 3 405B, for example, was trained on 15.6 trillion tokens across more than 16,000 H100 GPUs.
Another core insight: pre-training provides knowledge, post-training shapes behavior, and evaluation determines whether the model moves forward or returns for further adjustments.
The 8 phases:
- Planning and compute: scaling laws define model size, tokens, and FLOP budget.
- Data: collection, filtering, deduplication, domain mix, and tokenization.
- Architecture and small-scale experiments: Transformer, RoPE, GQA, MoE.
- Pre-training: predicting the next token using thousands of accelerators and checkpoints.
- Post-training: SFT, RLHF, DPO, and RL with verifiable rewards.
- Safety and evaluation: benchmarks, red teaming, and risk thresholds.
- Optimization for deployment: quantization, distillation, and inference.
- Weight release: risk assessment, model card, licensing, and distribution.
On each slide, I detail the inputs, outputs, objective, rationale, and key importance of every step. The deck also features a sequence workflow diagram, a summary table of techniques, a glossary, and 41 references.
💡 My GRC perspective: every phase serves as a control point. Data provenance and licensing, evaluation evidence, release decision-making, and version traceability form the bedrock of trustworthy and auditable AI.
Closed-source labs rarely release weights: the overall workflow applies to both, with only Phase 8 differing.
Save this post for future reference and share it with colleagues and fellow AI researchers.
Which phase do you consider the most underrated, and why? Let me know in the comments below! 👇