A collection of 6 posts
Quick answer. Ministral 3B is Mistral's smallest Mistral 3 model — Apache 2.0, runs on a laptop CPU or 6-8 GB GPU at Q4_K_M, and hits about 385 tok/s on an RTX 5090. Install in one command with Ollama, LM Studio, or llama.cpp. Pick
Last updated: May 1, 2026. Executive Summary Running Mistral 3 8B locally empowers users with privacy, speed, and cost efficiency. Heading into 2026, Mistral 3 8B remains a standout among small LLMs (Large Language Models) for performance, low hardware requirements, and competitive pricing, making it a compelling choice for developers,
Quick answer. Run Devstral 2 with Ollama using the official tags: ollama pull devstral-small-2 (24B, 68.0% SWE-bench Verified, fits a 24 GB RTX 4090 or 32 GB Mac at Q4_K_M) or ollama pull devstral-2 (123B, 72.2% SWE-bench Verified, needs 4×24 GB VRAM or a single
Devstral, Mistral AI’s cutting-edge agentic coding model, is redefining the boundaries of automated software engineering. Whether you’re a hobbyist developer, a seasoned enterprise engineer, or a research scientist, Devstral offers unprecedented capabilities that streamline and scale complex coding workflows. Want the full picture? Read our continuously-updated AI Coding
Meta's Llama 4 and Mistral AI's Mistral 7B sit at two very different points on the open-model spectrum. Llama 4 is a large, Mixture-of-Experts family built for long context, multimodal input, and frontier-level reasoning. Mistral 7B is a compact, dense model built to run fast and
Quick answer. Install Ollama on macOS, run `ollama pull mistral` then `ollama run mistral`. Mistral 7B (Q4_K_M, ~4.1 GB) runs on any 16 GB Apple Silicon Mac at roughly 20-30 tok/s. LM Studio works too for a GUI. For Mistral's current flagship, see Mistral