The UI is a visual builder for the API. Anything you click, you can script.
On-demand GPUs
Each job runs on a dedicated GPU provisioned just for it. RTX 3090/4090/5090-class for smaller models, 80 GB datacenter cards above 16B. Spun up on demand, closed when the work ends.
OpenAI-compatible API
Files, fine-tuning jobs, chat completions. The same request and response shapes as OpenAI. Point your existing SDK at api.volition.network and it works.
LoRA, SFT and DPO
Supervised fine-tuning and preference tuning on curated Qwen3 bases or any open Hugging Face causal LM up to ~32B. Adapters are ~10 MB and download as tarballs.
Live training events
Per-step loss and DPO reward margins stream to the dashboard and the events API. Checkpoints save as you go; interrupted jobs resume from the last one.
Scale-from-zero serving
Finished models are servable immediately: vLLM nodes spin up on first request, share a GPU across adapters, and close after 30 idle minutes.
Metered usage & spend limits
Token-level metering on training and inference, daily rollups, and per-org spend limits that stop runaway costs before they happen.