ollama VS gpt4all

Download: Go to https://ollama.com/ and click on the download link for your operating system.

Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine.

To solve these, we built a stack that prioritizes integrity over ease. The centerpiece is Ollama, running the mxbai-embed-large model locally. This is the engine that translates human thought into high-dimensional coordinates.

Ollama for model serving

Fully offline: Point it at Ollama or LM Studio. Zero cost, nothing leaves your network.

Recommended hardware: The RTX 3060 with 12 GB VRAM is the budget king here — all these models fit with room to spare for KV cache overhead, even Gemma 4:12B (which needs ~8.5–9 GB with overhead). An RTX 4060 Ti 16 GB gives you more headroom. On the Apple side, any M2 or M3 MacBook with 16 GB unified memory handles these models comfortably via Ollama's Metal backend.

This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b. Same tools, same workflows, no data leaving your network.

Related: 35B MoE on 2× 1080 Ti · Ollama

The sample is a tiny console app running entirely against a local Ollama model — no cloud keys, and every HTTP call is traced so I can see exactly what goes over the wire (complete sample code). There's a single skill on disk:

irm https://ollama.com/install.ps1 | iex

GPT4All GitHub — 72K+ stars

GPT4All: also a solution with UI, simple, has fewer features than ollama/llama.cpp

The model must exist in GPT4All's model path. On arch this is ~/.local/share/nomic.ai/GPT4All/. An entry for this model must exist in models.json. You can use the metadata provided by nomic or specify your own in the following format if your model is not listed. The GPT4All wiki provides find guidance on configuring custom models.

Hi it's me again! Over the past few days, I've been testing multiples ways to work with LLMs locally, and so far, Ollama was the best tool (ignoring UI and other QoL aspects) for setting up a fast environment to test code and features. I've tried GPT4ALL and other tools before, but they seem overly bloated when the goal is simply to set up a running model to connect with a LangChain API (on Windows with WSL).

https://github.com/nomic-ai/gpt4all support OS: Windows, Linux, MacOS

Generative AI is hot, and ChatGPT4all is an exciting open-source option. It allows you to run your own language model without needing proprietary APIs, enabling a private and customizable experience.

GPT4All for an even easier gui

https://github.com/nomic-ai/gpt4all

GPT4ALL is built upon privacy, security, and no internet-required principles. Users can install it on Mac, Windows, and Ubuntu. Compared to Jan or LM Studio, GPT4ALL has more monthly downloads, GitHub Stars, and active users.

From the moment Llama 3.1 was released, GPT4All developers have been working hard to make a beta version of tool calling available. We're happy to announce that the beta is now ready. The first tool is web search implemented through brave.com just as in the Llama 3.1 paper.

A wiki has been made to walk users through the setup here: https://github.com/nomic-ai/gpt4all/wiki/Web-Search-Beta-Rel...

Join us on discord to give feedback and get help with the new Llama 3.1 Beta for GPT4All: https://discord.com/invite/4M2QFmTt2k

Thanks for taking the time to respond. I was thinking of something local, especially in light of:

Google's Gemini AI caught scanning Google Drive PDF files without permission https://news.ycombinator.com/item?id=40965892 .

Looks like GPT4All[1] and AnythingLLM[2] are worth exploring. There's also the closed-source macOS app RecurseChat[3,4] which appeared on HN a few months ago[5].

[1] https://github.com/nomic-ai/gpt4all

[2] https://github.com/Mintplex-Labs/anything-llm

[3] https://recurse.chat

[4] https://recurse.chat/blog/posts/local-docs

[5] https://news.ycombinator.com/item?id=39532367