ollama VS llamafile

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

For people looking for alternatives, I would also recommend llama-file, it’s a one file executable for any OS that includes your chosen model: https://github.com/mozilla-ai/llamafile?tab=readme-ov-file

It’s truly open source, backed by Mozilla, openly uses llama.cpp and was created by wizard Justine Tunney of CosmopolitanC fame.

Personally I'd start with llamafile [0] then move to compiling your own llama.cpp.

It's not as bad as you might think to compile llama.cpp for your target architecture and spin up an OpenAI compatible API endpoint. It even downloads the models for you.

[0]: https://github.com/mozilla-ai/llamafile

Mozilla is working on it again, and they're asking for input:

https://github.com/mozilla-ai/llamafile/discussions/809

> # Avoid issues when wine is installed.

> sudo su -c 'echo 0 > /proc/sys/fs/binfmt_misc/status'

Please don’t recommend this. If binfmt_misc is enabled, it’s probably for a reason, and disabling it will break things. I have a .NET/Mono app installed that it would break, for example—it’s definitely not just Wine.

If binfmt_misc is causing problems, the proper solution is to register the executable type. https://github.com/mozilla-ai/llamafile#linux describes steps.

I made myself a package containing /usr/bin/ape and the following /usr/lib/binfmt.d/ape.conf:

  :APE:M::MZqFpD::/usr/bin/ape:

Llamafile: https://github.com/Mozilla-Ocho/llamafile

ive approached the same thing but slightly differently. i can run it on consumer hardware for vastly cheaper than the cloud and don't have to worry about image sizes at all. offering 20,000 minutes of transcription for free up to the rate limit (1 Request Every 5 Seconds)

https://geppetto.app

I contributed "whisperfile" as a result of this: https://github.com/Mozilla-Ocho/llamafile/tree/main/whisper....

Seconded for Llamafile, here is a link for references https://github.com/Mozilla-Ocho/llamafile . It indeed is working on all major platforms and its tooling allows easy creating of new llamafiles with new models. The only caveat is Windows where there is a limit 4Gb for executable files so just a llamafile launcher and the gguf file itself must be used. But this approach will work anywhere anyway.