FlexBench: Adaptive AI Benchmarking

• FlexBench is a modular extension that reconceptualizes ML benchmarking by dynamically adapting models, configurations, and datasets for optimal AI performance.
• It integrates seamlessly with Hugging Face Hub and aggregates benchmarking metadata into the Open MLPerf Dataset for collaborative analytics.
• The platform measures key metrics such as accuracy, latency, throughput, energy use, and cost to guide resource-aware and cost-effective AI deployments.

FlexBench is a modular extension to MLPerf LLM inference benchmarking, designed to provide a dynamically adaptive platform for evaluating, optimizing, and deploying AI systems. It reconceptualizes benchmarking as a learning-driven process—where models, configurations, and datasets are continuously assessed and improved. By integrating with Hugging Face Hub and aggregating benchmarking metadata into the Open MLPerf Dataset, FlexBench supplies practitioners with actionable insights into accuracy, latency, throughput, energy consumption, and cost, thus enabling informed decisions for real-world AI deployments under diverse constraints (Fursin et al., 14 Sep 2025).

1. Conceptual Design and Architecture

FlexBench frames benchmarking as an AI task rather than a static protocol. This approach allows the platform to dynamically curate and optimize models across a heterogeneous spectrum of datasets, software stacks, and hardware environments. Key features include:

• Modular extension of MLPerf LLM inference: FlexBench builds atop established MLPerf methodology, inheriting the core inference benchmarking routines while extending them for flexible reconfiguration and rapid experimentation.
• Unified framework: Different models, datasets, and hardware resources can be seamlessly swapped through command-line parameters, with no need to modify the underlying codebase.
• Continuous adaptation: Benchmarking is treated as an ongoing process; new metadata, configurations, and performance results are added iteratively, mirroring the rapid evolution of AI hardware and models.

2. Integration with Hugging Face Hub

A defining architectural feature is FlexBench's integration with Hugging Face Hub:

• Dataset and model access: Users can select from Hugging Face's extensive model and dataset library, enabling evaluation of the latest LLMs and task domains without overhead.
• Collaborative and reproducible workflows: Scripts and tools are designed for effortless inclusion of emerging models or public datasets featured in Hugging Face, thus fostering reproducibility across diverse teams and deployments.
• Scalability: FlexBench supports benchmarking across both cloud and commodity hardware, enabling direct comparison and rapid scaling.

3. Benchmarking Metrics

FlexBench systematically tracks performance and resource efficiency via several metrics:

Metric	Description	Practical Use
Accuracy	Model correctness on specific tasks (e.g., ROUGE)	Quality assessment
Latency	Inference time and time-to-first-token (TTFT)	Responsiveness
Throughput	Tokens processed per second	System scalability
Energy Consumption	Power usage during inference	Sustainability
Cost	Hardware utilization and operational expenses	Economic efficiency

Metrics can be combined for cost-effectiveness analysis, e.g., throughput per dollar:

$T_{eff} = \frac{\text{Throughput (tokens/s)}}{\text{Cost (\$)}}$

This enables researchers and practitioners to quantitatively optimize both performance and economic constraints for large-scale AI deployments.

4. Open MLPerf Dataset and Collaborative Data Curation

FlexBench aggregates all benchmarking results—including both standard MLPerf runs and newly generated FlexBench outputs—into the Open MLPerf Dataset:

• Data aggregation: Historic MLPerf results are integrated with FlexBench outcomes, resulting in a standardized, feature-rich dataset containing performance, model, and system metadata.
• Feature engineering: Each record comprises harmonized and curated attributes—model size, data types, hardware profiles—facilitating predictive modeling and configuration analysis.
• Open accessibility: The dataset is shared on GitHub and Hugging Face, supporting community-driven updates, collaborative analytics, and extensibility.

5. Validation and Empirical Results

FlexBench has been validated through official MLPerf Inference submissions, including evaluations of DeepSeek R1 and LLaMA 3.3 models on mainstream server hardware (NVIDIA H100 GPUs). Validation highlights include:

• Rapid switching: Benchmarks for distinct models and datasets can be executed using simple command-line switches—demonstrating efficiency and user-centric design.
• Reliability: FlexBench results show minimal discrepancies when compared with native vLLM infrastructure outputs, confirming accuracy and dependability.
• Expanded metrics: FlexBench produces additional actionable metrics (accuracy, energy, cost), guiding optimization steps such as pruning and quantization, which are not traditionally captured by MLPerf.

6. Practical Applications and Deployment Strategies

FlexBench's benchmarking paradigm empowers practitioners with actionable analytics for system optimization and co-design:

• FlexBoard interface: A Gradio-based application, FlexBoard, visualizes the Open MLPerf Dataset and allows for predictive modeling to identify optimal hardware/software configurations under resource or performance constraints.
• Cost-effectiveness: Users can quantitatively balance trade-offs among accuracy, latency, throughput, energy, and cost—enabling resource-aware AI deployment for diverse environments from small-scale research to hyperscale data centers.
• Informed deployment decisions: The system guides selection of model architectures, optimization techniques, and hardware profiles tailored to specific operational requirements.

7. Ongoing Development and Future Research

FlexBench's development is iterative and community-driven. Planned advancements include:

• Broadened support: Extension to more models, datasets, and system configurations.
• Continuous dataset updates: Integration of the latest benchmarking runs and engineered features.
• Enhanced analytics: Incorporation of model graphs, tensor shapes, compiler optimization profiles, and accelerator details to enrich performance analysis.
• Community engagement: Strengthened by linking FlexBoard with the Collective Knowledge ecosystem and soliciting user feedback for evolving tool features.
• Energy efficiency: Alignment with hardware manufacturers to facilitate design of cost- and energy-efficient AI systems based on empirical benchmark data.

FlexBench thus represents a significant evolution of AI benchmarking, distinguished by its learning-driven methodology, open data curation, and practical focus on cost-effective, resource-aware deployment in the rapidly advancing AI ecosystem (Fursin et al., 14 Sep 2025).