A growing number of enterprises and tech companies want the control that comes with an open-weight model — but not the burden of standing up training and inference infrastructure themselves. Spacebus gives you dedicated GB300 NVL72 capacity to train, fine-tune, and serve your own open-weight models, on hardware you don't have to build or manage.
Open-weight models let organizations own their model the way they own their data — with full visibility into what it is, how it behaves, and where it runs.
Full visibility into model weights and architecture, instead of relying on a closed API you don't control.
Adapt a base model to your domain, your product, and your customers — without sending proprietary data through a third-party inference API.
Own the weights, and you're not exposed to a vendor changing pricing, deprecating a model, or shifting terms out from under you.
Serve inference from a regional Spacebus site instead of a distant, shared endpoint — cutting latency for production traffic.
Run pre-training, continued pre-training, or fine-tuning jobs on dedicated GB300 NVL72 clusters — your data and your checkpoints never leave infrastructure you've contracted exclusively.
Serve your fine-tuned or base open-weight model directly from a Spacebus site, with capacity sized to your production traffic and sited close to where that traffic originates.
Bring any open-weight base model — Llama, Qwen, DeepSeek, GPT-OSS, or your own architecture — or start from scratch with your own pre-training run.
Run your training or fine-tuning jobs on a contracted allocation of GB300 NVL72 clusters at the Spacebus site closest to your team or your data.
Deploy the resulting model for inference on the same network — sized, and sited, for the latency and throughput your product needs.
Retrain, fine-tune again, or swap in a newer open-weight base model as your product and the open ecosystem evolve — all on the same contracted capacity.
Tell us about the model you're training or serving, and we'll help you size the right capacity.
Talk to the team