ollama VS gemma_pytorch

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

Gemma: https://ai.google.dev/gemma

📖 Architecture Guidelines: Google DeepMind Official Gemma Developer Documentation

Gemma on Google AI

Google Gemma documentation — Official documentation for the Gemma model family and deployment context.

Core • https://github.com/google-deepmind/gemma • https://github.com/google/gemma_pytorch

That’s what I set out to build for the Google Gemma 3n Hackathon: a privacy-first, offline-ready AI NPC system, powered by Gemma 3n running locally via Ollama.

Google Gemma 3 Official Page

Google AI Gemma Docs

The evaluations included several state-of-the-art LLMs such as GPT-4o, Claude 3.5 Sonnet, and open-sourced Gemma models. The study found that LLMs performed well on GSM-Q and GSME-Q domains with over 80% accuracy, while struggling with Logic-Q and Planning-Q, where they barely exceeded 50% accuracy.

It's an older image that they just reused for the blog post. It's on https://ai.google.dev/gemma for example