Best Local LLM Runners (2026)

A local LLM is a large language model that runs entirely on your own hardware — your laptop, desktop, or home server — instead of through a cloud API. By running models locally, you get complete privacy (no data leaves your machine), zero API costs, offline access, and full control over which models you use and how they're configured.

Thanks to advances in model quantization (GGUF, GPTQ) and efficient inference engines like llama.cpp, it's now practical to run capable models on consumer hardware. Tools like Ollama, LM Studio, and Jan make the process as simple as downloading an app and picking a model — no machine learning expertise required.

The right tool depends on your technical comfort level and use case:

• Desktop Apps (LM Studio, Jan, GPT4All, Msty, Pinokio) — Best for getting started quickly. Download an app, pick a model, and start chatting. Graphical interfaces make model management easy with no terminal knowledge needed.
• CLI Tools (Ollama) — Best for developers who want a lightweight, scriptable tool. Ollama's single-command interface makes it easy to pull and run models, and its OpenAI-compatible API integrates with hundreds of existing tools.
• Web UIs (Open WebUI, AnythingLLM) — Best for teams or power users who want a ChatGPT-like experience with local models. Self-hosted web interfaces support multi-user access, RAG pipelines, and custom workflows.
• Frameworks (llama.cpp, LocalAI) — Best for developers building custom applications. These provide the inference engine that powers most other tools, giving you maximum performance tuning and flexibility.

The hardware you need depends on the model size. As a rough guide for quantized (Q4) models:

• 7B parameter models (Llama 3.1 7B, Mistral 7B) — 8GB RAM minimum, runs well on most modern laptops. Fast enough for interactive chat on CPU.
• 13-14B parameter models (Llama 3.1 14B) — 16GB RAM recommended. Good balance of quality and speed on mid-range hardware.
• 30-34B parameter models — 32GB RAM or a GPU with 24GB VRAM (RTX 3090/4090). Significantly better quality, but slower on CPU-only setups.
• 70B+ parameter models (Llama 3.1 70B) — 48-64GB RAM or multiple GPUs. Near-frontier quality but requires serious hardware investment.

Apple Silicon Macs (M1/M2/M3/M4) are particularly well-suited for local LLMs thanks to their unified memory architecture, which lets models use all available RAM as VRAM. A MacBook Pro with 32GB can comfortably run 30B models.