Open MLPerf Dataset Overview

• Open MLPerf Dataset is a public repository of benchmark results, metadata, and system configurations that standardizes AI evaluation.
• It supports diverse workflows like FlexBench, enabling real-time predictive modeling and feature engineering for performance and deployment optimization.
• The dataset integrates comprehensive metrics such as accuracy, latency, throughput, energy, and cost to inform hardware/software co-design and system optimization.

The Open MLPerf Dataset is an extensible, publicly available repository of benchmarking results and metadata standardized for AI system evaluation, optimization, and deployment decision-making. It originated as a central collection point for MLPerf benchmark outputs, and is now foundational for modern, dynamic benchmarking workflows such as FlexBench, supporting predictive modeling and feature engineering for both academic and industry practitioners (Fursin et al., 14 Sep 2025).

1. Structure, Representation, and Integration

The Open MLPerf Dataset is organized as a modular collection of JSON records, each encapsulating key benchmarking results, metrics, and system configuration details. A typical record comprises fields like:

• metrics.accuracy (e.g., ROUGE scores or similar quality metrics)
• metrics.result and metrics.result_per_accelerator (e.g., tokens/sec or images/sec)
• model.architecture, model.name, model.number_of_parameters
• software.framework, system.cpu.model, system.accelerator.name, interconnect specifics

This modular schema enables the aggregation of heterogeneous benchmarking data—including legacy MLPerf submissions and new FlexBench outputs—while supporting extensibility for future tasks and model variations.

FlexBench, a modular extension of the MLPerf LLM inference benchmark, is tightly integrated with the dataset. It operates a client–server architecture: MLPerf LoadGen drives real-world inference patterns against running servers (e.g., vLLM), and performance data is captured, standardized, and ingested into the dataset. Hosting is open (GitHub, Hugging Face), and community-driven curation is encouraged to maintain relevance.

2. Role in AI System Benchmarking and Predictive Analytics

The Open MLPerf Dataset advances benchmarking by framing it as a learning task: aggregated results from diverse software and hardware configurations are used not only to report absolute performance, but also to power predictive models that can guide AI deployment decisions (Fursin et al., 14 Sep 2025). This approach reflects a paradigm shift—from static, episodic benchmarks to continuous, data-driven infrastructure supporting:

• Comparative analysis across platforms, frameworks, and models
• Informed selection of hardware/software stacks for target workloads
• Planning for cost-effective, resource-constrained AI system deployment, tuned to throughput, latency, and power targets

A plausible implication is that as benchmarking data accumulates, the dataset enables online or batch model-based recommendations, optimizing workload placement or configuration according to user constraints.

3. Key Metrics, Evaluation Paradigms, and Feature Engineering

Entries in the Open MLPerf Dataset record a spectrum of metrics instrumental for technical benchmarking and downstream ML-driven analytics:

Metric	Description	Typical Use
Accuracy	ROUGE, mAP, or task-specific quality score	Model evaluation, selection
Latency	MLPerf LoadGen statistics, TTFT	Interactivity, responsiveness
Throughput	Tokens/sec, images/sec, requests/sec	Capacity, system scaling
Energy Consumption	Power draw per inference	Efficiency, TCO estimation
Cost	Direct or derived cost estimation	Deployment planning

The dataset is designed for feature-rich analytics. For example, one may extract compound features by combining hardware specifics (accelerator vendor, memory size) with throughput and latency, enabling predictive modeling of configuration efficiency. Optimization goals (as considered within FlexBench) can be formalized as weighted cost functions in LaTeX:

$$\min_{config} \; C(config) = \alpha \times \text{Latency} + \beta \times \frac{1}{\text{Throughput}} + \gamma \times \text{Energy} + \delta \times \text{Cost}$$

Tuning the coefficients $\alpha, \beta, \gamma, \delta$ supports scenario-specific optimization, such as minimizing latency under a power or budget cap.

4. Collaborative Curation, Openness, and Extension Mechanisms

The Open MLPerf Dataset is released under an Apache 2.0 license, supporting open, collaborative modification. Curators and practitioners are encouraged to clean, validate, and extend the schema to reflect evolving benchmarking needs. Schema elements may be added to incorporate new model characteristics (e.g., tensor shapes, compiler flags, model graphs) as use-cases diversify.

Continuous updates integrate both historic MLPerf results and newer FlexBench outputs, maintaining representativeness across the rapidly moving hardware, model, and software landscape. Collaborative platforms (FlexBoard) provide visualization and analysis tools, facilitating comparative studies and configuration exploration.

A plausible implication is that this open curation model supports rapid benchmarking adaptation to emerging AI paradigms (for instance, novel model architectures or cloud-native deployment patterns), further accelerating translation from research to production.

5. Applications: Predictive Modeling, Deployment Optimization, and Co-design

The dataset enables several classes of real-world applications:

• Performance Prediction: Using historical and current benchmarking records, one can train meta-models that estimate expected throughput, latency, energy use, or cost for new AI workloads on candidate deployments.
• Configuration Optimization: Visualization tools (e.g., FlexBoard) allow practitioners to explore the configuration space interactively, identify Pareto-optimal points, and run “what-if” analyses for different budget or power constraints.
• Software/Hardware Co-design: By leveraging feature-rich metadata, researchers can analyze correlations between specific system parameters and workload performance, guiding iterative improvements in both model design (e.g., quantization, pruning) and hardware (e.g., accelerator selection, system topology).

This suggests that with sufficient data, automated benchmarking-driven configuration selection may replace ad hoc or manually tuned deployment strategies in both enterprise and scientific AI settings.

6. Technical and Methodological Context

The Open MLPerf Dataset is contextualized within broader benchmarking traditions—such as standardized MLPerf datasets for training, inference, and domain-specific tasks (e.g., LLMs, tiny ML, HPC workloads) (Mattson et al., 2019, Reddi et al., 2020, Farrell et al., 2021, Banbury et al., 2021). Its design reflects contemporary best practices:

• Use of standardized, public datasets (ImageNet, COCO, Wikipedia, etc.) to ensure reproducibility and fairness (Mattson et al., 2019, Reddi et al., 2020)
• Integration with process automation tools and modular, portable scripts (as in the Collective Mind and CM4MLPerf frameworks) for technology-agnostic benchmarking (Fursin, 24 Jun 2024)
• Support for workload diversity, including structured data tasks, vision, recommendation, LLM inference, and edge ML (Fursin et al., 14 Sep 2025, Banbury et al., 2021, Reddi et al., 2020)

The Open MLPerf Dataset, by enabling granular benchmarking and metadata aggregation, supports continuous learning about optimal AI system design and operation, and forms a critical substrate for the next generation of AI benchmarking and optimization frameworks.

7. Limitations and Future Directions

While the Open MLPerf Dataset already covers accuracy, latency, throughput, energy, and cost, a plausible implication is that future extensions may address additional metrics such as fairness, robustness, or interpretability. As benchmarking methodologies—such as benchmarking-as-learning tasks—grow in importance, the dataset will likely become more tightly coupled to automated optimization systems and active-learning-driven workflow selection.

Potential limitations may arise from incomplete coverage of emerging workloads or rapidly shifting hardware/software architectures; collaborative curation and modular schema extension are intended to mitigate such risks. Ongoing integration with predictive modeling pipelines, visualization tools, and deployment optimization frameworks is expected to further expand its utility in research and production environments.