Running powerful AI language models on your own PC is now genuinely practical. You get privacy, zero subscription fees, and full control over your data. This guide breaks down the tools you can install today, the best free open-source models to run on them, and which combinations work best for which use cases. The local-LLM landscape splits into two layers, and you typically need one tool from each:
- Runners / inference engines — software that loads and executes the model weights on your hardware (Ollama, LM Studio, llama.cpp).
- Front-ends / coding agents — the chat UI, IDE extension, or terminal agent you actually interact with (Continue, Cline, OpenCode, Claude Code).
For example, Ollama (runner) + Continue.dev (VS Code extension) + Qwen2.5-Coder (model) is a complete free local coding setup.
Tools to Run Open-Source LLMs Locally
Runners and Desktop Apps
These are the engines that actually load and execute the model on your machine.
| Tool | Type | Best For | Platform | Notes |
|---|---|---|---|---|
| Ollama | CLI + local API server | Most users; the de-facto standard | Win / macOS / Linux | One of the most popular tools for running LLMs locally. One-line model installs. Backbone for most front-ends below. |
| LM Studio | Desktop GUI | Beginners who want point-and-click | Win / macOS / Linux | Polished GUI with a model browser, built-in chat, and an OpenAI-compatible local server. |
| llama.cpp | C++ inference engine | Power users, CPU-only setups | Win / macOS / Linux | The engine that powers Ollama, LM Studio, and GPT4All under the hood. |
| Jan | Desktop app (open source) | “Offline ChatGPT” experience | Win / macOS / Linux | Clean ChatGPT-style UI; supports multiple models and can act as a local API server. |
| GPT4All | Desktop app | Lightweight chat on modest hardware | Win / macOS / Linux | Focuses on simplicity and accessibility with smaller models. |
| Text Generation WebUI (oobabooga) | Web UI | Tinkerers and fine-tuning | Win / macOS / Linux | Flexible browser-based interface supporting many architectures. |
| LocalAI | Self-hosted server | Drop-in replacement for the OpenAI API | Linux / Docker | Lets existing apps that talk to OpenAI run against local models. |
| vLLM | High-performance server | Multi-user serving, production | Linux + NVIDIA GPU | Built for high throughput; overkill for a single-user PC. |
Coding Agents and IDE Integrations
This is the “Claude Code / Cursor” category — tools that read your code, execute commands, and edit files agentically.
| Tool | Type | Free? | Local LLM Support | Best For |
|---|---|---|---|---|
| Claude Code (Anthropic) | Terminal agent | CLI is free, requires paid plan ($20+/mo) or API credits | ❌ Claude only | Highest-quality agentic coding; SWE-bench leader at 80.8% |
| OpenCode (SST) | Terminal agent (TUI) | ✅ MIT license | ✅ Full Ollama support, 75+ providers | Closest open-source equivalent to Claude Code; best free agentic CLI |
| Cursor | VS Code fork (IDE) | Free tier; Pro $20/mo | ⚠️ Limited | IDE-native experience for VS Code lovers |
| Continue.dev | VS Code / JetBrains extension | ✅ Apache 2.0 | ✅ First-class Ollama / LM Studio | Lightweight chat + tab autocomplete inside your IDE, fully local |
| Cline (formerly Claude Dev) | VS Code extension | ✅ Free, open source | ✅ Ollama supported | Most popular open-source coding extension; 5M+ installs across multiple editors |
| Roo Code / Kilo Code | VS Code extension (Cline forks) | ✅ Free | ✅ | Multi-mode agents (Code/Architect/Ask/Debug); newer multi-agent orchestration |
| Aider | Terminal agent | ✅ Free, open source | ✅ Ollama supported | Git-native pair programming; commits each change automatically |
| GitHub Copilot Chat | Built into VS Code | Free tier; $10/mo full | ✅ Ollama models in picker (v0.18.3+) | Native VS Code experience that can now use local models |
| Tabby | Self-hosted server | ✅ Open source | ✅ | Team / on-premise Copilot replacement |
| Goose (Block) | Desktop app + agent | ✅ Apache 2.0 | ✅ Ollama | Hybrid local/cloud, plugs into Zed/JetBrains/VS Code |
Quick clarification: “Visual Studio” by itself is an IDE, not a local-LLM tool. To do local AI in VS Code, install one of the extensions above. Ollama + Continue.dev is the most popular free combo.
Best No-Cost Open-Source LLMs in 2026
All models below are free to download and run locally. Click any model name to open its official page on Hugging Face or the publisher’s site. Hardware requirements assume 4-bit quantized (Q4_K_M) versions, which is the standard for local use. As a rough rule: 8 GB VRAM runs 7–8B models, 24 GB VRAM runs 30B-class models, and 40 GB+ is needed for 70B-class models.
| Model | Size | Best For | Strengths | Hardware (Q4) | License |
|---|---|---|---|---|---|
| Llama 3.1 8B Instruct (Meta) | 8B | General-purpose all-rounder | Fast, capable, supported by every tool | 8 GB RAM/VRAM | Llama Community |
| Qwen2.5-Coder 7B (Alibaba) | 7B | Coding (chat + autocomplete) | Top open 7B coder; sizes from 1.5B to 32B | 8 GB | Apache 2.0 |
| Qwen3 / Qwen3.5 (Alibaba) | 30B MoE (~3B active) | Coding and reasoning on a laptop | Beats GPT-5-mini on many benchmarks; runs on a MacBook with 64GB RAM | 24 GB VRAM or 64 GB Apple Silicon | Apache 2.0 |
| DeepSeek V3 / V4 | 685B MoE (~37B active) | Frontier reasoning (server-class) | Excellent multilingual and reasoning | Server-class | Open weights |
| DeepSeek-Coder V2 | 16B / 236B | Heavy-duty coding | Specialized for code | 16 GB+ | Open weights |
| Mistral 7B v0.3 | 7B | Instruction following, agents | Strong at following instructions precisely | 8 GB | Apache 2.0 |
| Gemma 4 (Google) | 26B MoE (~4B active) | Fast local chat on consumer hardware | ~85 tokens/sec on AMD Ryzen AI MAX+ | 16–32 GB | Gemma terms |
| GPT-OSS (OpenAI open-weight) | Various | Reasoning, structured outputs | Strong tool-like behavior | Varies | Open weights |
| Phi-3 / Phi-4 (Microsoft) | 3.8B / 14B | Edge devices, 8 GB laptops | Punches above its weight class | 4–8 GB | MIT |
| Kimi K2.6 (Moonshot AI) | 1T MoE | Top open-source coding (via API) | Strongest open-weight coding model in current rankings | Server-class | Modified MIT |
| StarCoder2 | 3B / 7B / 15B | IDE tab-autocomplete | Specialized for code completion | 4–16 GB | OpenRAIL |
| Nomic Embed Text | 137M | Embeddings for RAG / @codebase | The standard local embedding model | <1 GB | Apache 2.0 |
Quick Reference: Tool Docs and Install Tutorials
Bookmarks for every tool covered in this post. The “Install Tutorial” column links to a YouTube search for current install walkthroughs — top results stay fresh as new videos are published, so you’ll always see up-to-date guides for your OS.
Quick Decision Guide
Not sure where to start? Match your situation to the recommended stack below.
| Your Situation | Recommended Stack |
|---|---|
| “I just want a chatbot offline, no fuss” | LM Studio + Llama 3.1 8B |
| “Free AI coding in VS Code” | Ollama + Continue.dev + Qwen2.5-Coder |
| “Autonomous agent that edits my files, free” | OpenCode + Ollama or Cline + Ollama |
| “Best coding agent and I don’t mind paying” | Claude Code (highest benchmarks) |
| “VS Code with AI, cloud is fine” | Cursor ($20/mo) or Copilot ($10/mo) |
| “On-prem / team setup” | Tabby or vLLM |
| “Build apps against a local LLM” | Ollama API or LocalAI |
Hardware Reality Check
- 8 GB VRAM / 16 GB RAM: 7–8B models, fine for chat and autocomplete.
- 16 GB VRAM / 32 GB RAM: opens up 13B and small-MoE models.
- 24 GB VRAM (RTX 3090/4090): runs 30B-class models well; the sweet spot.
- CPU-only with 32–64 GB DDR5: works at 10–18 tokens/sec — slow but usable.
- Apple Silicon (M-series, 32 GB+ unified memory): excellent across model sizes.
Final Thoughts
The local-LLM ecosystem has matured to the point where running a capable AI assistant on your own hardware is a practical daily-use option, not a weekend experiment. Pair Ollama with Continue.dev or OpenCode, drop in a Qwen2.5-Coder or Llama 3.1 model, and you have a private, no-subscription coding assistant that handles most everyday tasks. For frontier-level work, paid tools like Claude Code still hold an edge, but the gap is narrowing every month.
