Local LLM Selector 🖥️

Find the best local LLM for your hardware. Compare Ollama, LM Studio, DeepSeek, Llama, and more for privacy-first AI.

Why Local LLM Selector Is Worth Using

Find the best local LLM for your hardware. Compare Llama, Mistral, Phi, and more by VRAM, speed, and quality. Free — no signup. This page is built for people who want a fast path to a working result, not a vague prompt-and-pray workflow. If you need a more reliable first draft, cleaner output, or a repeatable workflow you can hand to a teammate, Local LLM Selector is designed to shorten that path.

Most visitors use Local LLM Selector because they need something specific done now: a deliverable, a decision, or a workflow checkpoint. The sections below show the fastest way to get value from the tool and the adjacent pages that help you keep going.

How to Use Local LLM Selector

Find the perfect local LLM for your setup:

1Enter your hardware specs — GPU model, VRAM, and RAM.
2Select your intended use case — coding, chat, creative writing, or research.
3The tool recommends the best models that fit your hardware constraints.
4View recommended quantization levels and expected performance for each model.

Who Is Local LLM Selector For?

For anyone running AI models locally.

Privacy-Focused Developers

Run AI models locally without sending data to external APIs.

AI Hobbyists

Find the best model that actually runs smoothly on your consumer GPU.

Researchers

Match model requirements to lab hardware for experiments.

Enterprise Teams

Plan hardware procurement for local LLM deployment.

What a Good Result Looks Like

A strong outcome from Local LLM Selector is not just “some output.” It should be usable with minimal cleanup, aligned to the task you opened the page for, and specific enough that you can paste it into the next step of your workflow without rewriting everything from scratch.

If the first pass feels too generic, use the use cases, FAQs, and related pages here to tighten the scope. That usually produces better results faster than starting over in a blank chat.

Frequently Asked Questions

Which models are in the database?▼

Llama 3, Mistral, Phi-3, Gemma, Qwen, Yi, and dozens of other open-source models with all size variants.

Does it account for quantization?▼

Yes. Shows VRAM for FP16, INT8, INT4, and GGUF quantizations with quality tradeoff estimates.

How accurate are the recommendations?▼

Based on official model specs and community benchmarks. Actual results depend on your specific hardware and software setup.

Does it include setup instructions?▼

Yes. Each recommendation includes links to download models and setup guides for Ollama, llama.cpp, and vLLM.