Ollama

Install Docker Model Runner, run your first local LLM, hit the OpenAI-compatible API, push models as OCI artifacts, and see when to switch from Ollama.

Turn your local Ollama models into a private ChatGPT with Open WebUI: one-command Docker install, document chat (RAG), voice, and safe LAN access.

Ornith 1.0 is DeepReinforce's open-source, self-scaffolding family of agentic coding models, post-trained on Qwen 3.5 and Gemma 4. This guide shows how to run each variant locally - 9B on a laptop, 35B MoE on a 24GB card, 397B on an 8-GPU box - with Ollama, LM Studio and vLLM, plus agent settings.

A 1,620-upvote 'Stop using Ollama' thread set off a real 2026 debate about switching. Here is why developers are leaving, who is overstating it, and which alternative to pick by use case: GUI, speed, production, or Apple Silicon.

A practical 2026 guide to turning a Mac mini or Mac Studio into a headless local-LLM server: a memory-tier to model-size table, how to serve Ollama, LM Studio, and MLX to your network, realistic tokens/sec, and what to buy.

May 2026 was a heavy ship month for local AI runtimes. Ollama added Codex App support. vLLM 0.21 stabilised DeepSeek V4 on Blackwell. llama.cpp merged MTP speculative decoding. MLX hit 4x faster on M5. LM Studio shipped stable MTP. Practical runtime-by-runtime changelog.

Honest 2026 comparison of the five dominant local LLM runtimes: Ollama, LM Studio, vLLM, llama.cpp, and MLX. Throughput numbers, feature matrix, and a decision tree.

Quick answer. To run Gemma 4 on Windows, install Ollama from ollama.com, open PowerShell, and run ollama pull gemma4:e4b followed by ollama run gemma4:e4b. The E4B (~9.6 GB) variant fits comfortably on 16 GB systems. For a GUI, install LM Studio, search “gemma 4”, download a

What LM Studio is, how to install it on Mac, Windows and Linux, how the OpenAI-compatible server works, MLX vs llama.cpp on Apple Silicon, document chat (RAG), the lms CLI, and where it beats Ollama and llama.cpp.

Quick answer. Cherry Studio is a free, open-source desktop app that gives you one chat window across 60+ AI model providers plus local models run through Ollama, LM Studio or vLLM. It runs on Windows, macOS and Linux, adds knowledge-base RAG, MCP tools and autonomous agents, and stores everything on

A senior-engineer walkthrough of running Cline or Continue.dev fully local against Ollama on a 24GB GPU — exact config, VRAM tables, and an honest quality reality check vs cloud Claude.

What OmniCoder 9B is, its lineage and license, vendor-reported benchmarks, the full GGUF quant table, and step-by-step Ollama and llama.cpp setup.

A 2026 decision framework for vLLM, Ollama, and LM Studio — when each one wins on throughput, hardware support, and cost, with cited benchmarks instead of fabricated numbers.

Most comparisons treat OpenClaw, LM Studio, and Ollama as rivals. They're not — they're three layers of a local AI developer stack. Here's how to choose and configure the right combination for your hardware and workflow in 2026.

Run a private, zero-cost personal AI assistant on your own hardware using OpenClaw and Ollama. This guide covers hardware tiers, model selection, the fastest setup path, and the configuration mistakes that break tool calling.

Learn how to install Void AI, the open-source Cursor alternative, and run it with local models via Ollama or LM Studio — with zero cloud dependencies.

Void AI is an open-source, VS Code-based code editor that brings Cursor-style AI features — inline editing, agent mode, and autocomplete — without routing your code through a proprietary backend. Here's what it does and who should use it.

A hardware-first comparison of Gemma 4 and Llama 4 for local deployment in 2026. Includes full VRAM tables, benchmark data, licensing analysis, and a use-case decision matrix to help you pick the right model for your machine.

A complete step-by-step guide to running Gemma 4 locally with Ollama — covering all four model sizes, context configuration, the Ollama REST API, and troubleshooting on Mac, Linux, and Windows.

Google Gemma 4 is here — Apache 2.0 licensed, #3 globally on Arena AI, and running locally in minutes. This review covers every variant, real benchmark numbers, and step-by-step local setup.

Learn how to install, run, benchmark, and compare Qwen3.5‑0.8B with OpenClaw and Ollama on your CPU for free. Private, local AI with practical demos.

Learn how to install and run OpenClaw with LM Studio local models completely free. Complete setup guide with step-by-step instructions, performance benchmarks, hardware requirements, and comparison with competitors. Works offline with full data privacy.

Learn how to install OpenClaw with Ollama local models. Step-by-step setup guide with system requirements, benchmarks, pricing, and comparison with competitors.

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