Tools, frameworks, and models for local AI
9 resources found for "Ollama"
Bespoke Labs' 9B decision model: it runs entirely locally via Ollama 0.35 and answers typed questions (true/false, numeric scale, multiple choice) through the new systemone API. Built for fast classification in automated pipelines, no cloud.
Intel: Arc (Xe) GPUs are the third consumer platform for local AI, with 12-16GB of VRAM at entry prices. The maintained inference path is Vulkan (native Ollama, llama.cpp): the IPEX-LLM project was archived in January 2026. The GPUs in the catalog are the child resources.
La prima scheda Intel da prendere sul serio per l'IA locale: Xe2 Battlemage, 12GB GDDR6 a 456 GB/s, 190W. 12GB a prezzo d'ingresso: i modelli 8-14B in Q4 stanno interamente in VRAM. Il software è il punto debole: IPEX-LLM è stato archiviato a gennaio 2026, il percorso mantenuto oggi è Vulkan (Ollama nativo, llama.cpp) con ~40-42 tok/s su 8B Q4.
The first open-source MoE of OpenBMB's MiniCPM-V series: 35.2B total parameters, ~3B active per token, multi-image and video understanding with early visual-token compression (LLaVA-UHD v4). First open-source MoE with fluid on-device decoding — 13+ tok/s on 3GB of RAM on an iPhone 17 Pro Max — thanks to Lookahead Expert Scheduling (llama.cpp-omni), fine per-level quantization and DSpark speculative sampling. Day-0 support for Rockchip, Houmo, Intel, AMD and Arm; runs on Ollama, llama.cpp, vLLM and SGLang.
GTK4/libadwaita app for GNOME to chat with local models via Ollama, in Rust under GPLv3: it handles Ollama setup, models, and loading local documents and images.
The runtimes that run local models on your hardware: Ollama for simplicity, llama.cpp for portability, vLLM for production throughput. The engines in the catalog are the child resources.
Full ChatGPT-style web interface for Ollama and llama.cpp: multi-user accounts, organized conversations, document management for RAG, per-model system prompts, tools and plugins. Version 0.11.4 (September 2026) makes file metadata travel with the retrieved chunks and runs hybrid search natively in the database: large knowledge bases are much faster. The simplest way to turn your local AI into an everyday product.
A zero-dependency Python tool that recognizes and summarizes every file with a local LLM (Ollama, LM Studio or MLX-LM), fully offline. Detects type by content, extracts structure (CSV, email, scripts, logs) and reports files carrying instructions to the assistant. Works without a model (--no-llm). MIT license.
