Benchmarking Playbook: Baselines, Ablations, Data Splits, Metrics, and LLM Inference TCO

Context#

Reliable benchmarks are the backbone of machine learning progress. Establishing a strong baseline, evaluating with disciplined data practices, selecting metrics that reflect user value, and running ablation studies that truly isolate effects are essential. For large language models, tying performance and latency targets to an inference total cost of ownership (TCO) model aligns technical choices with business constraints.

Baselines First#

• Always define a baseline as the first comparison point for all experiments.
• Suitable options include existing non-ML solutions (e.g., rule-based approaches) or simple heuristics.
• The baseline anchors expectations and prevents overfitting evaluation to complex models.

Data Management for Evaluation#

• Split data into training, development (validation), and evaluation (test) sets.
• Keep the evaluation set strictly held out to avoid leakage and to preserve an unbiased measure of generalization.
• Use the development set to make iteration decisions; reserve the evaluation set for final measurements only.

Metrics That Reflect User Value#

• Choose metrics before development begins and keep them stable.
• Align metrics directly with user-perceived value and costs.
• Avoid midstream metric changes that can obscure real progress or regressions.

Ablation Studies That Isolate Effects#

• Definition: Systematically remove or alter specific components, layers, modules, or features to measure their individual contribution to performance.
• Core principles:
• Isolate variables by changing one component at a time while holding all others constant.
• Keep experimental conditions consistent to ensure comparability.
• Practical approach:
• Identify distinct components of the system to vary.
• Design a controlled sequence of ablations.
• Measure and compare results against the baseline and the full model.

LLM Inference TCO Inputs to Tie Benchmarks to Costs#

• Model and hardware specifics:
• Model size (e.g., 70B).
• GPU type (e.g., H100/H200) and VRAM per GPU.
• Performance targets:
• Throughput: Prefill TPS/GPU and Decode TPS/GPU.
• Latency SLO: Define acceptable end-to-end latency targets.
• Integrate these inputs into your benchmarking plan so throughput and latency measurements map cleanly to infrastructure and cost implications.

Practical Checklist#

• Baseline defined and documented (e.g., rule-based or simple heuristic).
• Clear training/dev/evaluation split with a strictly held-out evaluation set.
• Pre-registered metrics aligned to user value; no mid-experiment changes.
• Ablation plan that changes one component at a time under consistent conditions.
• LLM inference TCO inputs specified: model size, GPU type/VRAM, throughput (prefill/decode), and latency SLO.

Takeaways#

• Start simple with a baseline; it provides a stable anchor for all future comparisons.
• Protect the evaluation set to keep your benchmark honest.
• Let user value guide metric selection and stick to it.
• Use ablations to understand what truly drives performance.
• Connect performance and latency measurements to TCO to make informed, end-to-end decisions.