{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-training-compute-optimal-large-language-models-chinchilla","slug":"training-compute-optimal-large-language-models-chinchilla","name":"Training Compute-Optimal Large Language Models (Chinchilla)","type":"product","url":"https://arxiv.org/abs/2203.15556","page_url":"https://unfragile.ai/training-compute-optimal-large-language-models-chinchilla","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-training-compute-optimal-large-language-models-chinchilla__cap_0","uri":"capability://planning.reasoning.compute.optimal.model.scaling.with.token.to.parameter.ratio.optimization","name":"compute-optimal model scaling with token-to-parameter ratio optimization","description":"Determines the mathematically optimal allocation of training compute budget between model parameters and training tokens using empirical scaling laws derived from training runs across multiple model sizes. The approach fits power-law relationships to observed loss curves, then solves for the compute-optimal ratio where both parameters and tokens scale equally with total compute budget (N ≈ C/6L, D ≈ 20C/L where C is compute budget). This differs from prior Kaplan scaling laws which suggested undertrained models; Chinchilla shows equal parameter-token scaling is optimal.","intents":["Determine how many parameters and tokens to use given a fixed training compute budget","Understand whether to train larger models on fewer tokens or smaller models on more tokens","Predict loss curves and training efficiency across different model scales","Allocate resources between model size and dataset size for a new training run"],"best_for":["ML researchers designing new LLM training runs","ML engineers planning compute budgets for foundation models","Teams deciding between scaling parameters vs data for fixed compute"],"limitations":["Scaling laws derived from specific training setups (dense transformer architectures, particular optimization hyperparameters); may not transfer perfectly to sparse models, mixture-of-experts, or different training regimes","Empirical fitting introduces uncertainty bounds; predictions degrade significantly beyond the range of observed model sizes (roughly 70M to 540B parameters in the paper)","Does not account for inference cost optimization or downstream task performance variance; optimizes only for training loss","Assumes homogeneous data quality and standard cross-entropy loss; does not model domain-specific or curriculum learning effects"],"requires":["Access to compute infrastructure for training multiple model scales (70M, 400M, 1B, 3B, 7B, 13B, 70B parameter models minimum)","Standardized training pipeline with reproducible hyperparameters across scales","Ability to measure loss curves with sufficient precision across training steps"],"input_types":["training compute budget (in FLOPs)","model architecture specification (transformer depth, width, attention heads)","dataset size and composition"],"output_types":["optimal model parameter count","optimal training token count","predicted loss at convergence","training efficiency curves"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-training-compute-optimal-large-language-models-chinchilla__cap_1","uri":"capability://data.processing.analysis.loss.prediction.across.model.scales.via.empirical.scaling.law.interpolation","name":"loss prediction across model scales via empirical scaling law interpolation","description":"Predicts training loss for unseen model sizes by fitting power-law functions (L(N,D) = aN^α + bD^β + E) to loss measurements from trained models at multiple scales, then interpolating or extrapolating to new parameter/token combinations. The model captures how loss decreases with both parameter count and data size, enabling loss prediction without retraining. Chinchilla's key finding is that optimal loss follows L_opt(C) = E + (C/6L)^-α where both exponents are approximately -0.07.","intents":["Predict final training loss for a proposed model size without running full training","Compare loss outcomes across different parameter-token allocation strategies","Estimate convergence behavior before committing compute budget","Identify diminishing returns regions where additional compute yields minimal loss improvement"],"best_for":["ML researchers planning experiments and allocating compute budgets","Teams evaluating whether to scale up existing models","Organizations comparing multiple model architecture proposals"],"limitations":["Accuracy degrades significantly outside the training range of observed scales; extrapolation beyond 540B parameters is unreliable","Assumes smooth power-law relationships; does not capture phase transitions, emergent capabilities, or task-specific performance cliffs","Fitted exponents (α ≈ -0.07) are specific to dense transformer architectures and may not apply to sparse models, recurrent architectures, or hybrid approaches","Does not predict downstream task performance or generalization; only training loss on the specific training distribution"],"requires":["Training loss measurements from at least 3-4 different model scales","Consistent training setup across scales (same optimizer, learning rate schedule, data preprocessing)","Sufficient training steps to reach convergence or near-convergence at each scale"],"input_types":["model parameter count (N)","training token count (D)","observed training loss values at multiple (N,D) pairs"],"output_types":["predicted loss for arbitrary (N,D) combinations","loss sensitivity curves showing impact of parameter vs token scaling","optimal allocation recommendations for a given compute budget"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-training-compute-optimal-large-language-models-chinchilla__cap_2","uri":"capability://planning.reasoning.compute.budget.allocation.solver.for.parameter.token.tradeoff","name":"compute budget allocation solver for parameter-token tradeoff","description":"Given a fixed training compute budget (measured in FLOPs), solves for the optimal split between model parameters (N) and training tokens (D) by applying the derived scaling law relationships. The solver uses the constraint that compute C ≈ 6ND (accounting for forward and backward passes) and the empirical finding that optimal allocation has N ≈ C/6L and D ≈ 20C/L, where L is the sequence length. This produces a deterministic recommendation for model size and dataset size given compute budget.","intents":["Given a compute budget in FLOPs, determine the optimal model size in parameters","Given a compute budget, determine how many tokens to train on","Decide whether to train a 70B model on 1.4T tokens or a 7B model on 140B tokens for the same compute","Plan resource allocation for a new training run with fixed hardware and time constraints"],"best_for":["ML engineers planning training infrastructure and hardware procurement","Teams with fixed compute budgets (e.g., limited GPU hours or cloud credits)","Organizations comparing cost-efficiency of different model sizes"],"limitations":["Assumes linear relationship between compute and FLOPs; does not account for hardware efficiency variations, communication overhead, or distributed training inefficiencies","Optimal allocation is for training loss only; does not optimize for inference cost, latency, or downstream task performance","Assumes standard dense transformer architecture; does not apply to sparse models, mixture-of-experts, or other architectural variants","Does not account for practical constraints like memory limitations, batch size requirements, or hardware availability"],"requires":["Compute budget specified in FLOPs or equivalent (e.g., GPU hours × TFLOPS)","Knowledge of sequence length (L) for the training setup","Assumption of standard transformer training efficiency (6 FLOPs per parameter per token)"],"input_types":["total training compute budget (in FLOPs)","sequence length (tokens per example)","optional: constraints on minimum/maximum model size"],"output_types":["optimal model parameter count","optimal training token count","estimated final training loss","compute efficiency metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-training-compute-optimal-large-language-models-chinchilla__cap_3","uri":"capability://data.processing.analysis.empirical.scaling.law.fitting.and.validation.across.model.scales","name":"empirical scaling law fitting and validation across model scales","description":"Trains multiple model instances at different scales (70M, 400M, 1B, 3B, 7B, 13B, 70B parameters) with varying token counts, measures training loss curves, and fits power-law functions to the observed data. The fitting process uses least-squares regression on log-log plots to extract scaling exponents and coefficients, then validates the fit by comparing predicted vs observed loss on held-out model sizes. This creates an empirical foundation for all downstream scaling law predictions and recommendations.","intents":["Establish empirical scaling laws for a specific model architecture or training setup","Validate whether published scaling laws apply to your particular training configuration","Measure the actual scaling exponents for your models rather than relying on published values","Create organization-specific scaling law models for internal planning"],"best_for":["ML researchers conducting scaling studies","Large organizations training multiple foundation models","Teams needing to validate scaling laws for custom architectures or datasets"],"limitations":["Requires training 6-10 models at different scales, consuming significant compute (hundreds of thousands of GPU hours for large models)","Fitting accuracy depends on number of data points and range of scales; sparse or narrow ranges produce unreliable exponents","Power-law fitting assumes smooth relationships; does not capture non-monotonic behavior or phase transitions","Scaling laws are specific to the training setup (optimizer, learning rate schedule, data distribution); changing these requires refitting"],"requires":["Access to substantial compute infrastructure (minimum 100K GPU hours for comprehensive study)","Standardized training pipeline with reproducible hyperparameters across all scales","Ability to measure loss curves with high precision across thousands of training steps","Statistical tools for power-law fitting and validation (e.g., scipy, numpy)"],"input_types":["model sizes to train (parameter counts)","token counts to train on","training configuration (optimizer, learning rate, batch size, etc.)","training data"],"output_types":["fitted power-law coefficients and exponents","loss curves for each model size","validation metrics (R² fit quality, prediction error)","scaling law equations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-training-compute-optimal-large-language-models-chinchilla__cap_4","uri":"capability://data.processing.analysis.training.efficiency.benchmarking.and.comparison.across.scales","name":"training efficiency benchmarking and comparison across scales","description":"Measures and compares training efficiency metrics (loss per compute unit, convergence speed, sample efficiency) across different model sizes and token counts. Efficiency is quantified as the loss achieved per unit of compute (FLOPs), enabling direct comparison of whether larger models or more tokens provide better returns on compute investment. The benchmarking reveals that compute-optimal allocation (equal parameter-token scaling) achieves better efficiency than either parameter-heavy or token-heavy alternatives.","intents":["Compare the compute efficiency of different model size choices","Determine whether to invest compute in more parameters or more tokens","Measure how much compute is wasted by suboptimal parameter-token allocation","Benchmark your training setup against published scaling law baselines"],"best_for":["ML engineers optimizing training efficiency","Teams comparing different model architecture proposals","Organizations evaluating whether to scale up or scale out"],"limitations":["Efficiency metrics are specific to training loss; do not reflect downstream task performance, inference cost, or latency","Benchmarking requires training multiple models to completion, consuming substantial compute","Efficiency comparisons assume identical training conditions (optimizer, data, hyperparameters); differences in setup can confound results","Does not account for practical efficiency losses from distributed training, communication overhead, or hardware utilization variations"],"requires":["Training runs for multiple model sizes and token counts","Precise measurement of compute usage (FLOPs, GPU hours, wall-clock time)","Standardized evaluation methodology across all runs"],"input_types":["training loss curves for multiple model sizes","compute budget for each training run","model parameter counts and token counts"],"output_types":["efficiency metrics (loss per FLOP, loss per GPU-hour)","efficiency comparison tables","recommendations for optimal allocation","efficiency curves showing diminishing returns"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"low","permissions":["Access to compute infrastructure for training multiple model scales (70M, 400M, 1B, 3B, 7B, 13B, 70B parameter models minimum)","Standardized training pipeline with reproducible hyperparameters across scales","Ability to measure loss curves with sufficient precision across training steps","Training loss measurements from at least 3-4 different model scales","Consistent training setup across scales (same optimizer, learning rate schedule, data preprocessing)","Sufficient training steps to reach convergence or near-convergence at each scale","Compute budget specified in FLOPs or equivalent (e.g., GPU hours × TFLOPS)","Knowledge of sequence length (L) for the training setup","Assumption of standard transformer training efficiency (6 FLOPs per parameter per token)","Access to substantial compute infrastructure (minimum 100K GPU hours for comprehensive study)"],"failure_modes":["Scaling laws derived from specific training setups (dense transformer architectures, particular optimization hyperparameters); 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