Take your fine-tuned model to production — in seconds, not minutes.
You fine-tuned a great model. Now you have to serve it — and the usual stack means heavyweight runtimes, minutes-long cold starts, and a GPU bill that only makes sense at constant high traffic.
Outcomes
Products in this stack
AI infrastructure
Drop-in inference, embeddings and an assistant that understands your data and your schema.
The challenge
- Cold starts of 1–2 minutes make scale-to-zero impractical for bursty traffic.
- Inference runtimes eat VRAM, so you can't pack many replicas per GPU.
- Heavy dependency stacks (PyTorch, Triton, transformers) are slow to build and brittle to deploy.
The result
Ship your own model with the economics of scale-to-zero and the throughput of a production engine — powered by ZSE, our zero-dependency inference engine.
How it works
Convert your model to .zse
Quantize and package your fine-tuned checkpoint into a compact .zse artifact — INT4 weights, zero ML framework dependencies.
Deploy on the Zyora Server Engine
ZSE loads the model in seconds and serves an OpenAI-compatible endpoint. Scale to zero between bursts without paying the cold-start tax.
Pack more per GPU
Because ZSE serves in a fraction of the VRAM, you fit more replicas on the same hardware and cut your cost per token.
Ready to build this?
Start free today, or talk to us about the right setup for your team.