training compute budget

**Training compute budget** is the **total planned computational resources allocated to model training across all phases** - it sets hard constraints on achievable model size, token count, and experiment breadth. **What Is Training compute budget?** - **Definition**: Budget includes pretraining, validation, tuning, and infrastructure overhead. - **Cost Components**: GPU or TPU hours, storage I O, networking, and orchestration costs all contribute. - **Planning Role**: Determines feasible scaling envelope and experimental iteration cadence. - **Tradeoff Surface**: Must balance model capacity, data volume, and reliability testing depth. **Why Training compute budget Matters** - **Strategic Control**: Budget decisions shape capability roadmap and release timelines. - **Efficiency**: Good planning prevents overtraining low-value runs and underfunding critical evals. - **Risk Management**: Reserves compute for recovery runs and safety evaluations. - **Stakeholder Alignment**: Creates transparent expectations for engineering and leadership. - **Comparability**: Enables fair performance assessments under matched resource limits. **How It Is Used in Practice** - **Scenario Modeling**: Build multiple budget plans with expected capability outcomes. - **Milestone Gates**: Release additional budget only after passing predefined quality thresholds. - **Telemetry**: Track real-time compute burn versus planned trajectory. Training compute budget is **a foundational planning control in large-scale model development** - training compute budget should be managed as a dynamic control system tied to measurable capability progress.