Local LLM Software — Guides & Comparisons by Use Case

A local LLM runs entirely on your own hardware — phone, laptop, desktop, or server — without sending prompts to any cloud service. ChatGPT runs on OpenAI servers and sends your prompts there. Local LLMs are private, work offline, and have no per-token cost; ChatGPT is faster on rare topics and requires no setup.

No. 4 GB RAM and an integrated GPU is enough for small models like Phi-4 Mini or Gemma 3 4B. 16 GB RAM and a midrange GPU (RTX 3060 12 GB or M3 Pro) covers most everyday workflows. Heavy power users want 24+ GB VRAM.

For everyday tasks (chat, summarization, common code) the gap is 5-15% in 2026. For frontier reasoning and very obscure knowledge, cloud models still lead. The cost-quality trade-off favors local for most users with private or sensitive data.

Yes. Apps like LLM Farm and Private LLM run Phi-4 Mini and Gemma 3 4B on iPhone 16+ and flagship Android devices. Performance is 8-15 tokens/sec — usable for chat, draft writing, and offline reference.

After hardware, marginal cost is just electricity — usually $1-3/month for moderate use. The hardware investment ranges from $0 (existing laptop) to ~$2,000 for a high-end build. Compared to $20-200/month SaaS subscriptions, payback is typically 8-24 months.

Yes — assuming the app does not telemeter prompts, which most do not. Verifiable via open-source apps (Jan, GPT4All, Ollama) where you can audit network traffic. The model file itself does not "phone home" — it is just weights on disk.

GPT4All has the simplest install (one click, runs on 8 GB RAM). LM Studio is the most feature-rich. Jan is best for privacy. See the dedicated LM Studio vs Jan vs GPT4All comparison for benchmarks on each.

Yes. Continue.dev + Ollama + Qwen3-Coder reaches 90-95% of GitHub Copilot quality on everyday TypeScript and Python work, with full code privacy. Hardware requirements are RTX 3060 12 GB or M3 Pro+ Mac.

Yes — once the model is downloaded, all inference is local. Useful for travel, restricted networks, secure environments, and anywhere internet is unreliable.

For GDPR/EU AI Act compliance: Ollama or vLLM running on dedicated hardware, paired with Jan (UI), Continue.dev (coding), and AnythingLLM (RAG). All open source, all auditable, all on-prem. Mistral Large is a strong EU-hosted alternative for hybrid setups.

Yes — the Local LLM Software Directory catalogues 350 tools across 14 categories (runtimes, desktop apps, RAG, coding, agents, mobile, and more) with description, licence, and link for each.

They solve different problems. SillyTavern is a roleplay and character-chat frontend built for persona management and long-form fiction. Open WebUI is a general-purpose chat and RAG frontend built for document Q&A and everyday assistant use. Pick SillyTavern for creative writing and character work; pick Open WebUI for research, RAG, and daily chat.

They are not competitors — Obsidian is a note-taking app, and Ollama is a local model runtime. Used together, Ollama runs the model locally while an Obsidian plugin sends your notes to it for summarization, Q&A, or writing assistance.