# Benchmarking

The Procyon AI Image Generation Benchmark is a benchmarking tool developed by UL Solutions, aimed at providing professional users with a consistent, accurate, and easy-to-understand workload to measure the inference performance of AI accelerators on devices. The benchmark has been developed in collaboration with several key industry players to ensure fair and comparable results across all supported hardware. It includes three tests to measure performance from low-power NPUs to high-end discrete GPUs. Users can configure and run tests through the Procyon application or command line, supporting a variety of inference engines including NVIDIA? TensorRT?, Intel? OpenVINO?, and ONNX with DirectML. The product is primarily designed for engineering teams to evaluate inference engine implementations and the general AI performance of specialized hardware. The pricing structure provides a free trial, with the formal version available on an annual site license basis, offered at a paid quote.

FlagPerf, developed collaboratively by the Zhiyuan Institute and AI hardware manufacturers, is an integrated AI hardware evaluation engine designed to establish an industry practice-oriented metric system for assessing the actual capabilities of AI hardware across software stack combinations (models + frameworks + compilers). The platform supports a multidimensional evaluation metric system covering scenarios of large model training and inference, and accommodates multiple training frameworks and inference engines, connecting AI hardware and software ecosystems.

Procyon AI Text Generation Benchmark is a specialized benchmarking tool designed for testing and evaluating the performance of local large language models (LLMs) in AI. It ensures that tests leverage the local AI acceleration hardware in the system through close collaboration with leaders in the AI hardware and software field. This tool simplifies PC performance comparison and cost rationalization, validates and standardizes PC performance, and streamlines IT teams' PC lifecycle management, enabling quick decisions to enhance PC performance, reduce hardware costs, and save testing time.

Procyon AI Inference Benchmark for Android is a benchmarking tool based on NNAPI, designed to measure AI performance and quality on Android devices. It executes common machine vision tasks through a series of popular, state-of-the-art neural network models, enabling engineering teams to independently and standardize their evaluation of NNAPI implementations and the AI performance of dedicated mobile hardware. This tool not only measures the performance of dedicated AI processing hardware on Android devices but also validates the quality of NNAPI implementations, which is significant for optimizing hardware accelerator drivers and comparing the performance of floating-point and integer optimized models.

MLPerf Client is a newly developed benchmark created in collaboration with MLCommons, aimed at evaluating the performance of large language models (LLMs) and other AI workloads on personal computers (from laptops to desktops to workstations). This benchmark simulates real-world AI tasks to provide clear metrics on how systems handle generative AI workloads. The MLPerf Client working group hopes this benchmark will drive innovation and competition, ensuring that personal computers can meet the challenges of an AI-driven future.

P-MMEval is a multilingual benchmark that encompasses datasets focused on foundational and capability specialization. It extends existing benchmarks to ensure consistency in language coverage and provides parallel samples across various languages, supporting up to 10 languages from 8 language families. P-MMEval facilitates comprehensive assessment of multilingual capabilities and comparative analysis of cross-language transferability.

PARTNR is a large-scale benchmarking initiative released by Meta FAIR, which includes 100,000 natural language tasks aimed at studying multi-agent reasoning and planning. PARTNR utilizes large language models (LLMs) to generate tasks while minimizing errors through simulation loops. It also supports evaluations of AI agents in collaboration with real human partners, facilitated through human-in-the-loop infrastructure. PARTNR reveals significant limitations of existing LLM-based planners in task coordination, tracking, and recovery from errors, with humans solving 93% of tasks compared to just 30% for LLMs.

Cheating LLM Benchmarks is a research initiative aimed at exploring cheating behaviors in automated language model (LLM) benchmarking by constructing what are known as 'null models.' The project’s experiments have revealed that even simple null models can achieve high win rates in these benchmarks, challenging the validity and reliability of current benchmarking practices. This research is crucial for understanding the limitations of current language models and improving benchmarking methodologies.

Turtle Benchmark is a new, cheat-proof benchmark based on the 'Turtle Soup' game, focusing on the assessment of large language models (LLMs) in terms of logical reasoning and context comprehension. By eliminating the need for background knowledge, it provides objective and unbiased test results with quantifiable outcomes, ensuring that models cannot be 'gamed' through the use of real user-generated questions.

llm-colosseum is an innovative benchmarking tool that uses the game Street Fighter 3 to assess the real-time decision-making capabilities of large language models (LLMs). Unlike traditional benchmarking methods, this tool tests the models' quick responses, intelligent strategies, creative thinking, adaptability, and resilience through simulated real game scenarios.

DCLM-baseline is a pretraining dataset for language model benchmarking, containing 4T tokens and 3B documents. It is curated from the Common Crawl dataset after a careful planning of data cleaning, filtering, and deduplication steps, aiming to demonstrate the importance of data curation in training efficient language models. The dataset is only for research purposes and should not be used in production environments or for training domain-specific models, such as those for code and mathematics.

LAMDA-TALENT is a comprehensive tabular data analysis toolbox and benchmarking platform that integrates over 20 deep learning methods, 10 traditional methods, and 300+ diverse tabular datasets. This toolbox aims to enhance model performance on tabular data, offers robust preprocessing capabilities, optimizes data learning, and supports user-friendly and adaptable operations suitable for both novice and expert data scientists.

GenAI-Arena is a platform for benchmarking visual generation models in the wild. Users can anonymously participate in competitions, compare the performance of target models, and vote for the superior model. The platform supports anonymous model duels in different fields, helping users find the optimal image generation model for their needs. Users can click "New Round" to start a new duel and vote by clicking buttons to select the better model.

Maintained by the FreedomIntelligence organization, the Apollo project aims to democratize medical AI by providing large language models (LLMs) for the medical domain in multiple languages, covering 600 million people. The project includes models, datasets, benchmarks, and related code.