AI Scaling Laws

AI Scaling Laws — Compute-Optimal Sizing Skill

Functional reference for pre-training researchers and engineers who need to reason cold about compute, token, and parameter tradeoffs. Covers Kaplan et al. (2020), Chinchilla / Hoffmann et al. (2022), GPT-3 sizing, over-training for inference efficiency, and the mechanics of budget allocation for a from-scratch run.

This is a standard interview probe. Know the key ratios and be ready to work through a concrete sizing calculation without a lookup.

Quick Reference

Key distinction: Chinchilla-optimal minimizes validation loss for a given compute budget. It is not inference-optimal. Over-training a smaller model to more tokens gives a model that is cheaper per inference call, even though it spent more of the compute budget on data than the loss-optimal split would dictate.

ASCII Flow

Fix compute budget C (e.g. GPU-hours × FLOPs/hour)
           |
           v
C ≈ 6 N D  →  Solve for Chinchilla-optimal pair
           N* ≈ sqrt(C / 120),  D* ≈ 20 × N*
           |
           v
Adjust for inference cost constraint
  If inference is expensive: shrink N, increase D (over-train)
  If training cost dominates: stay near Chinchilla-optimal
           |
           v
Set token budget D = training tokens
  Constrain by data availability — data quality shifts the curve
  Verify token count is achievable from your corpus
           |
           v
Choose batch size, LR schedule, warmup to fill D tokens

When to Use This Skill

• Answering: "How many tokens should I train on for a model of size X?"
• Answering: "Given a GPU budget of Y A100-hours, what model size and token count should I target?"
• Evaluating whether a published training run is compute-optimal, over-trained, or under-trained
• Sizing a GPT-2 reproduction or any from-scratch experiment
• Interview preparation: compute/data/param tradeoff reasoning

Scope Boundaries

Depth on adjacent topics lives in these skills:

• Pre-training implementation, data pipelines, optimizer config → ai-pretraining
• Distributed training, tensor/pipeline parallelism, MFU → ai-distributed-training
• Token budget sourcing, deduplication, quality filters → ai-data-curation-pretraining
• Model architecture, post-training, deployment → ai-llm

Sizing Workflow

1. Fix compute budget C in FLOPs. Multiply GPU-hours by peak FLOPs/s and hardware utilization (MFU; typically 30–50% for realistic training).
2. Apply C ≈ 6 N D to enumerate feasible (N, D) pairs along the iso-compute curve.
3. Pick Chinchilla-optimal point: D* ≈ 20 × N (equivalently N* ≈ D / 20). For the exact coefficients use Hoffmann et al. Table A3.
4. Check data availability: if your corpus yields fewer than D* tokens at acceptable quality, you are data-constrained; shrink N or accept suboptimal allocation.
5. Adjust for inference regime: if you will serve the model at high QPS, favor a smaller N trained on more tokens (over-trained relative to Chinchilla). If training cost is the binding constraint, stay near Chinchilla-optimal.
6. Set hyperparameters: peak LR typically scales as ~N^{-0.5} (rough heuristic); batch size scales with D; cosine decay or trapezoidal schedule over D tokens.

Worked Example: GPT-2 124M Reproduction on 8× A100s

Given:

• 8 × A100 80GB, ~312 TFLOP/s each at BF16
• Rough MFU: ~40% (realistic for a small well-tuned training script)
• Available training time: ~8 GPU-hours (≈ 1 hour wall-clock on 8 GPUs)

Step 1 — Compute budget C:

C = 8 GPUs × 312e12 FLOP/s × 0.40 MFU × 8 × 3600 s
  ≈ 8 × 312e12 × 0.40 × 28800
  ≈ 2.9e19 FLOPs   (≈ 29 PetaFLOP)

Step 2 — N for GPT-2 124M: N ≈ 124e6 params (non-embedding; the embedding matrix ~38M is conventionally excluded from scaling law N; total is ~162M but N ≈ 124M is the standard usage)

Step 3 — Chinchilla-optimal D:

D* ≈ 20 × N = 20 × 124e6 ≈ 2.5B tokens

Step 4 — Sanity-check with C ≈ 6 N D:

C_needed = 6 × 124e6 × 2.5e9 ≈ 1.86e18 FLOPs

Our 8-GPU 8-hour budget of ~2.9e19 FLOPs comfortably covers this — we have ≈15× more compute than strictly needed for Chinchilla-optimal at 124M. This means we could:

• Train for far fewer hours (≈ 30 min wall-clock), or
• Over-train to 3–5B tokens to improve the model cheaply while we have the budget.

Conclusion: For a GPT-2 124M reproduction at this budget, target 2–3B tokens minimum; the budget supports over-training to 5–10B tokens if data is available, yielding a better model at no extra GPU cost.

Note: All figures above are approximate. MFU, batch size, and sequence length all affect realized throughput. Verify your actual tokens/second empirically before locking a training schedule.

Known Traps

1. Using Kaplan (2020) ratios after Chinchilla corrected them. Kaplan suggested scaling models much faster than data (~D ∝ N^{0.74}). Chinchilla showed equal scaling. GPT-3 is a canonical example of a Kaplan-era undercooked model: 175B params but only ~300B tokens, whereas Chinchilla-optimal would require ~3.5T tokens.

2. Conflating Chinchilla-optimal with inference-optimal. The Chinchilla optimum minimizes training loss for a fixed compute budget. Llama, Mistral, and most open-weight models deliberately over-train smaller models because inference cost matters more than training cost for deployed models.

3. Conflating total parameters with non-embedding parameters. Scaling laws in Kaplan and Chinchilla use N = non-embedding parameters. For models with large vocabularies, the embedding table can be 20–30% of total parameters. Use the non-embedding count in the C ≈ 6ND formula.

4. Ignoring that data is often the binding constraint. High-quality deduplicated text in a specific domain is finite. When the corpus cannot provide D* tokens at acceptable quality, the model is data-constrained regardless of the compute budget. Data quality shifts the effective loss curve — better data means lower loss at the same N and D.

5. Treating the 20:1 heuristic as a universal law. The exact coefficient varies with model architecture, data quality, and what loss metric is being optimized. The 20:1 ratio is the central Chinchilla finding but empirical fits from Epoch AI and others show the true optimum is sensitive to these factors.

6. Quoting Chinchilla's published fit constants as ground truth. Besiroglu et al. (2024) showed the Approach-3 fit (α≈0.336, β≈0.283) is biased by a pre-convergence optimizer and rounding; corrected exponents are α≈0.35, β≈0.37. Use the corrected values for projections.

7. Saying "Kaplan was wrong." The Kaplan/Chinchilla split is methodological (FLOP counting, warmup, optimizer tuning); correcting those reproduces Chinchilla's C^0.50 from Kaplan's setup (Pearce & Song 2024; Porian et al. 2024).

8. Ignoring inference cost when sizing a deployed model. Chinchilla optimizes training loss for training compute only. For anything served at scale, minimize total train+serve compute — which means smaller-and-longer than Chinchilla (Sardana & Frankle 2024).

9. Assuming the param count is precision-independent. Low-precision training lowers the effective param count, and PTQ damage grows with training tokens (Kumar et al. 2024). Plan quantization headroom alongside the token budget.

Common Anti-Patterns

• Choosing model size from a published architecture (e.g., "I want to do a GPT-2 run") without computing whether the training token budget is sufficient for that size.
• Setting training tokens to a round number (e.g., "1B tokens") without reference to the model size.
• Reporting parameter count without specifying whether embedding parameters are included.
• Assuming the Chinchilla-optimal training run produces the best inference-time model.
• Extrapolating scaling law curves beyond the range of the original data without acknowledging the extrapolation.

Core Principles

1. Hold compute constant when comparing. Scaling law comparisons are only meaningful on an iso-compute curve. Comparing a bigger model trained for fewer steps to a smaller model trained for more steps conflates model size with compute.

2. Tokens and parameters scale together. The Chinchilla result is not "more data is better." It is that the ratio D/N should be ~20 when compute is the constraint. Both must scale proportionally.

3. The optimum is a ratio, not a number. There is no universal "correct" model size. Given a fixed compute budget, there is an optimal (N, D) pair. Given a fixed inference budget, the optimal N is smaller (more over-training).

4. Data quality shifts the loss curve. Two training runs with the same N and D but different data quality will reach different loss values. Scaling laws are fit on specific corpora; the exact coefficients do not transfer directly to new domains without re-fitting or empirical validation.

Post-Chinchilla Developments (2023–2026)

Kaplan and Chinchilla are foundations, not the current frontier. A July-2026 answer is incomplete without these. Full detail in post-chinchilla-developments.md.

1. Chinchilla's published fit was corrected (Besiroglu et al. 2024). The Approach-3 constants are biased (pre-convergence optimizer + rounding); corrected exponents are α≈0.35, β≈0.37. The ~20:1 heuristic survives as an order-of-magnitude rule; the exact constants do not.
2. Repeating data is nearly free up to ~4 epochs (Muennighoff et al. 2023). When data-bound, train a smaller model for more epochs rather than under-feeding a Chinchilla-sized model. Marginal value of repeats decays to ~0 past ~16 epochs.
3. Inference-aware scaling (Sardana & Frankle 2024). Account for serving cost: deployed models should be smaller and trained far longer than Chinchilla-optimal. Quality improves out to ~10,000 tok/param.
4. Over-training is the deployed norm. Llama 3 8B used 15T tokens (~1,875 tok/param), ~75× Chinchilla-optimal, deliberately, for inference efficiency.
5. Precision-aware scaling (Kumar et al. 2024). Low-precision training lowers effective param count; PTQ damage grows with training tokens, so over-training can hurt a heavily-quantized deployment. Use effective (precision-adjusted) params.
6. MoE/sparsity scaling (Ludziejewski et al. 2024; Abnar et al. 2025). Apply 6ND with activated params; expert granularity and sparsity are first-class optimization knobs, not a footnote on a dense law.
7. Test-time-compute scaling (Snell et al. 2024). Inference compute (sampling + verification, search over reasoning) is a third axis, substitutable with pretraining: few-query/low-volume favors a small model + heavy test-time compute (can beat a ~14× larger model); high-volume favors pretraining bigger. The scaling law under the o1/o3 reasoning paradigm.
8. Distillation scaling (Busbridge et al. 2025). A law for distilled-student loss vs teacher/student compute split: distill when a teacher exists or you serve many students; train supervised when only one student is needed and the teacher must also be trained.
9. RL post-training compute — a fourth axis, still unsettled (Meta et al. 2025; arXiv:2509.25300, 2025; CoScale-RL 2026). Reasoning-model RL compute (rollouts + verifier + policy updates) follows a sigmoidal, recipe-dependent curve, not a clean Chinchilla-style power law. Don't quote a fixed RL-compute coefficient as settled science — this is the least mature scaling regime as of mid-2026.

Navigation: Core References

• Chinchilla Math — L(N,D) loss form, 20:1 derivation intuition, worked numbers from Hoffmann et al., the 2024 fit correction
• Compute Budget Estimation — C = 6ND derivation, FLOPs-to-GPU-hours conversion, worked table
• Post-Chinchilla Developments — 2023–2026 corrections and extensions: data-constrained, inference-aware, precision, MoE, Kaplan/Chinchilla reconciliation, test-time-compute and distillation scaling

External Sources

See data/sources.json for canonical papers and primary sources:

• Kaplan et al. "Scaling Laws for Neural Language Models" (arXiv 2001.08361)
• Hoffmann et al. "Training Compute-Optimal Large Language Models" / Chinchilla (arXiv 2203.15556)
• Besiroglu et al. "Chinchilla Scaling: A replication attempt" (arXiv 2404.10102) — corrects the Chinchilla fit
• Muennighoff et al. "Scaling Data-Constrained Language Models" (arXiv 2305.16264)
• Sardana & Frankle "Beyond Chinchilla-Optimal: Accounting for Inference" (arXiv 2401.00448)
• Kumar et al. "Scaling Laws for Precision" (arXiv 2411.04330)
• Brown et al. GPT-3 (arXiv 2005.14165)

Fact-Checking Rule

• Known bugs, regressions, framework/compiler/runtime footguns, and version-specific crash or workaround guidance must be verified against current primary web sources before being treated as current fact.
• Verify volatile external facts before final answers.
• Prefer official docs, standards, release notes, and pricing pages.
• If you cannot verify, say so explicitly and present the guidance as a dated assumption instead of a fact.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.