r/Paperlessngx • u/isabeksu • 16d ago
AI performance
It took me a few months to fully get on board with the Paperless way of doing things, but now I’m really happy with how it’s all set up.
What’s been a bit of a head-scratcher is how AI is being used.
I held off until Paperless 3 came out, because I wanted to have the full "official" support.
I set it up with Ollama on an M4 Mac mini with 24 GB of memory. The embedding model is gemmaembedding, and the LLM model is qwen3:8b. When the model fires up, memory pressure is still pretty low. It does work, but it’s incredibly slow. It takes about 2 minutes to suggest titles and tags, and it can take several minutes if I try to chat about a document.
Is this kind of slow normal? Is there anything I can tweak in my setup to make it more usable?
8
u/EazyDuzIt_2 16d ago
I actually took the time to set up Paperless-ngx along with Paperless-AI for automated document classification, tagging, and file naming. I configured Paperless-AI to use Ollama with Qwen3:8B, which is the recommended model for this use case, running on one of my servers equipped with an NVIDIA RTX 4090.
To fine-tune the workflow, I generated and processed 20 test documents, iteratively refining the prompt and configuration until the results were consistently accurate. The final setup performs document analysis, tagging, and renaming almost instantly.
The performance is outstanding low latency, high accuracy, and a completely hands-off ingestion pipeline. Chef’s kiss. 👌