Find AI Tools
Open dataset of real screenshot

What is Open dataset of real?

Anubis OSS is a specialise tool designed to benchmark and optimise Large Language Model (LLM) performance specifically on Apple Silicon hardware (M1, M2, M3 chips and later). It provides developers and researchers with an open dataset of real-world LLM performance metrics across different Apple Silicon configurations, enabling informed decisions about model selection, optimization, and deployment on Mac systems. The tool addresses a critical gap in LLM performance documentation by offering transparent, reproducible benchmarking data for Apple's ARM-based processors, which have become increasingly popular for machine learning development and inference. Users can access thorough performance comparisons, optimization techniques, and best practices for running large language models efficiently on Apple Silicon hardware.

Key Features

Open dataset of real LLM performance benchmarks on Apple Silicon processors

Performance comparison tools across different M-series chip generations

Model optimization recommendations for Apple Silicon architecture

Inference speed and resource consumption metrics (CPU, memory, power usage)

Community-contributed benchmark results and use case studies

Real-world performance data from diverse LLM architectures and sizes

Pros & Cons

Advantages

  • Addresses critical gap in Apple Silicon-specific LLM performance data
  • Open dataset allows community contributions and reproducible research
  • Helps developers optimise models for local M-series chip execution
  • Freemium model makes baseline benchmarking data accessible to everyone
  • Practical data for cost-effective, privacy-preserving on-device LLM deployment

Limitations

  • Performance data may become outdated as new Apple Silicon chips and LLM versions are released
  • Limited to Apple Silicon benchmarks, not applicable for other hardware platforms
  • Freemium model may restrict access to advanced analysis or custom benchmarking features

Use Cases

Developers evaluating which LLM models to deploy on Mac applications

Researchers studying ARM-based chip performance for ML workloads

Mac users deciding whether to run language models locally versus via cloud APIs

ML engineers optimising inference pipelines for Apple Silicon devices

Software companies building AI features into macOS applications