QVAC vs Local LLMs: Full App SDK vs Running a Model Locally
A local LLM gives you text generation. QVAC adds search, OCR, and speech on-device through one unified SDK interface.
This one is less "A vs B" and more "clearing up a category confusion." A local LLM is a language model running on your own hardware. QVAC is a whole SDK that includes running local LLMs but wraps a lot more around them. If you're trying to build a real product, the gap between "I can run a model locally" and "I have shippable on-device AI" is exactly what this covers.
The one-line difference
"Local LLM" describes running a language model on your own device — usually via an engine like llama.cpp or a runtime like Ollama. QVAC is a cross-platform SDK that runs local LLMs plus embeddings, transcription, OCR, translation, and RAG, with model distribution and mobile support built in.
Side by side
| QVAC | A local LLM (raw / via engine) | |
|---|---|---|
| Runs an LLM on-device | Yes | Yes |
| Beyond text (OCR, speech, translation) | Yes, same interface | No — LLM only |
| Retrieval / RAG | Built-in | You assemble it yourself |
| Mobile (iOS/Android) | Yes, from one codebase | Varies; often not turnkey |
| Cross-platform from one codebase | Yes | You handle per-platform builds |
| Model distribution to users | Yes (incl. P2P) | You solve it yourself |
| It's a product-ready SDK | Yes | It's a building block |
What "local LLM" gets you
Running a local LLM is the foundation, and it's genuinely powerful: a capable language model on your own hardware, private and offline, no API bill. Engines like llama.cpp and runtimes like Ollama or LM Studio make this very accessible. If all you need is text generation on a machine you control — a chatbot, a summarizer, a coding helper — a local LLM by itself may be all you need.
Notably, QVAC's own text engine (QVAC Fabric) is a fork of llama.cpp, so it lives in the same ecosystem — it's not competing with local LLMs, it's built on the same lineage.
What QVAC adds on top
Real apps rarely need only text. A finance app needs OCR to read receipts, transcription to log expenses by voice, embeddings and retrieval to answer questions over your data, and a language model to write the answer — all on-device, ideally on both iOS and Android from one codebase. Stitching those together yourself from raw local-LLM pieces is a large engineering project. QVAC bundles them behind one interface, adds model distribution (including peer-to-peer), and runs the same code across platforms.
So the difference isn't "better model" — it's "just the model" versus "everything you need around the model to ship an app."
The honest trade-off
If your need is narrow — text generation on a machine you control — a bare local LLM or a runtime like Ollama is simpler and sufficient. If you're building a multi-feature, cross-platform, consumer-facing app, assembling that from raw local-LLM parts means reinventing what QVAC already provides. Pick the smallest tool that covers the job.
Our apps will need far more than text: OCR, voice, retrieval, and chat, on both iPhone and Android, all on-device. Building that on raw local-LLM engines would have meant writing (and maintaining) most of QVAC ourselves. Using QVAC let us focus on the product instead of the plumbing. Here's how it works.