Can I run AI locally?

kept by eddie mar 16

The site canirun.ai estimates which local AI models a given machine can run based on hardware specs, but commenters found its RAM detection and speed predictions inaccurate for modern hardware.

April 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac mini
A guide for running Gemma 4 models locally on a Mac Mini via Ollama, with commenters reporting that the 26B variant is too slow and memory-intensive for daily use, while smaller quantizations suffice for light tool-calling tasks.
also on LM Studio, Llama.cpp, #hardware, #developer-tools
Running local models is good now
Local agentic coding models have reached surprising quality and usability over the past six months, now offering ~75% of frontier-model accuracy for many development tasks on a 64GB M2 Mac.
also on LM Studio, Llama.cpp, #open-source, #developer-tools
We Got Claude to Fine-Tune an Open Source LLM
Hugging Face released an open-source 'skill' that lets Claude Code, Codex, and Gemini CLI autonomously fine-tune LLMs on cloud GPUs and push models to the Hub.
also on Llama.cpp, Hugging Face, #open-source, #developer-tools
Building a High-End AI Desktop
An engineer bought a discounted Grace-Hopper server, converted it to water cooling, and now runs 235B parameter models locally for under €9,000.
also on Llama.cpp, #hardware, #open-source, #ai, #developer-tools
9 theses on AI | Sarthak Munshi
AI progress is constrained by long-task reliability, labor reallocation, cost inefficiencies of general APIs, the declining value of raw coding skills, inadequate benchmark testing, the limits of formal verification without strong specs, memory-bound local hardware advantages, the shift from data to environment-driven training, and the rising competitiveness of US open-weight models.
also on Hugging Face, #open-source, #ai, #developer-tools