
LLM Speed: Pre-Purchase Hardware Benchmarking for Local AI
A performance benchmarking tool that estimates local LLM speeds across various hardware and model configurations.
Specialized hardware designed for AI workloads, including custom accelerators, ASICs, TPUs, and novel computing architectures optimized for machine learning inference and training.

A performance benchmarking tool that estimates local LLM speeds across various hardware and model configurations.

The M5 Ultra Mac Studio leverages unified memory and a quad-die architecture to become the definitive hardware for running high-performance local AI agents.

GLM-5.3-Flash provides frontier-level multimodal and coding capabilities at a tenth of the cost by optimizing its architecture for domestic AI hardware.

NVIDIA's Rubin architecture adopts 100% liquid cooling at higher temperatures to eliminate water waste and maximize energy efficiency in AI factories.
OpenAI has launched its first custom inference chip, Jalapeño, to lower costs and increase efficiency through vertical hardware integration.
Local AI models are powerful tools for private, specialized business tasks but lack the reliability and reasoning of frontier cloud models for autonomous engineering.

CrankGPT is a human-powered, local AI device that replaces big tech's cloud infrastructure with personal fitness and total data privacy.

Core AI is Apple's high-performance framework for deploying and optimizing neural networks on Apple silicon.

Google's new 8th-gen TPUs provide specialized, high-efficiency hardware for training and serving the next generation of reasoning AI agents.

A hardware compatibility tool that grades the local performance of AI models based on a user's specific GPU and VRAM configuration.

An analog, 3D-optical fixed-point computer co-designed with iterative models accelerates both AI inference and real-world optimization with high robustness and projected 100× energy-efficiency gains over GPUs.