AI Infrastructure / Local LLMs
A GitHub repository guide to running state-of-the-art LLMs on local hardware, with concrete build notes, cost breakdowns, and runnable configs for large models.
The page clearly presents a useful AI-adjacent project: a guide for running SOTA LLMs locally. The snapshot shows substantive README content with hardware recommendations, model choices, storage setup, PCIe switch details, and ready-to-run serving configs, giving enough evidence for a public listing.
The snapshot does not show complete step-by-step setup instructions, full run commands, or whether the configs have been tested across different hardware setups; some linked resources and scripts are referenced but not visible here.
Last evaluated 59 days ago. Current rank #1422. Down 1 spot in the rankings.
Anthropic’s research preview introduces the Model Hardware Standard (MHS), a shared spec for AI agents to control physical devices and orchestrate lab or manufacturing equipment. The page explains how the standard works, shows early lab examples, and positions it as an upcoming open source standard for hardware integra
Self-hosted, Kubernetes-native platform for distributed multi-model LLM inference. It includes an OpenAI-compatible API, installer flow, and infrastructure-as-code deployment with support for air-gapped environments.
AletheionAGI is a grounding and memory layer for production AI. It adds persistent context, authorized evidence, and fail-closed delivery so AI systems can block unsupported claims before they reach users.
