QVAC vs Ollama: App SDK vs Local Model Runtime
QVAC vs Ollama: an app SDK for shipping to phones vs a runtime for your desktop. Different jobs, both valid.
Both keep AI on your own hardware, and both are open-source — but they're built for different jobs. Ollama is a runtime for running and serving local models on a computer. QVAC is an SDK for embedding on-device AI inside a shipping app across mobile and desktop. If you're deciding between them, that distinction is the whole story.
The one-line difference
Ollama is a local LLM runtime: install it on a machine, pull a model, and talk to it over a command line or a local HTTP API. QVAC is an application SDK: you build multi-modal on-device AI directly into your iOS, Android, or desktop app from a single codebase.
Side by side
| QVAC | Ollama | |
|---|---|---|
| What it is | App-embeddable SDK | Local model runtime / server |
| Primary interface | SDK you build into an app (@qvac/sdk) | CLI + local REST API |
| Runs inside a mobile app? | Yes (iOS, Android) | Not designed for it — desktop/server-oriented |
| Modalities | LLM, embeddings, transcription, OCR, translation, RAG, more | Primarily LLM text (GGUF models) |
| Cross-platform mobile | Yes, from one codebase | No |
| P2P model distribution | Yes (Holepunch stack) | No |
| License | Apache 2.0 | MIT |
| Best for | Shipping consumer apps with on-device AI | Developers running/serving models on a machine |
Where Ollama is the stronger choice
If you want to run local models on your own computer — for a coding assistant, a home lab, a self-hosted endpoint your scripts hit, or quick experimentation — Ollama is excellent and hard to beat. Its "pull and run" workflow is famously simple, it exposes an OpenAI-compatible API that slots into existing tooling, and it runs headless on servers and in Docker. For developer workflows where a human or a script is talking to a model on a machine, it's a natural default.
Ollama has a massive ecosystem and huge community momentum, and dozens of frontends and orchestrators treat it as a first-class backend.
Where QVAC is the stronger choice
If your goal is to ship an app — especially a mobile app — with AI that runs on the end user's device, that's a different problem than running a model on your own machine. QVAC is built for it: the same code runs across iOS, Android, macOS, Windows, and Linux, and it goes beyond text to bundle transcription, OCR, embeddings, translation, and RAG behind one interface. That matters because real apps often need several of those at once — think receipt scanning plus categorization plus chat in one finance app.
QVAC also handles model distribution to end users (including peer-to-peer), and the no-login, download-a-model-on-first-run pattern is built for consumer onboarding rather than developer setup.
The honest trade-off
Ollama answers "how do I run a model on my machine?" QVAC answers "how do I put on-device AI inside the app I'm shipping to users, on every platform, across multiple modalities?" You could even use both at different stages — Ollama for local prototyping and evaluation, QVAC for what actually ships in the app.
We're building consumer apps for phones. We need transcription, OCR, embeddings, and chat running on the end user's device across iOS and Android from one codebase — not a runtime on our own machines. That's precisely the gap QVAC fills. Here's how it works.