Standing up training or inference infrastructure for an open-weight model used to mean weeks of GPU provisioning, cluster configuration, and manual tuning. On Spacebus, it's a single action: choose the model, and the platform handles the rest.
Select any open-weight model — a popular base model from the open ecosystem, or one you bring yourself — from the Spacebus model catalog.
Spacebus reads the model's architecture and automatically determines the right GPU allocation, parallelism, and precision to run it efficiently — no manual cluster math required.
The model is provisioned onto dedicated GB300 NVL72 capacity at the Spacebus site closest to your workload — never shared, spot-priced infrastructure.
A production-ready inference endpoint is live and callable, sized to the latency and throughput your application needs.
Launch a training or fine-tuning job on the same one-click flow — Spacebus provisions the GPU allocation your job needs and manages it through to checkpoint.
Move straight from a fine-tuned checkpoint to a live, production-grade inference endpoint, without a separate infrastructure request or manual handoff.
One-click deployment isn't a thin wrapper over raw GPUs — it's a purpose-built platform layer engineered for open-weight models specifically.
Automatically calculates GPU count, parallelism strategy, and precision from the model's own architecture — the manual sizing work is done for you.
Built on proven, open-source inference engines, coordinated across nodes for high-throughput, low-latency serving at scale.
Every deployment runs on infrastructure isolated at the hardware level — not a shared multi-tenant pool — consistent with Spacebus's sovereign capabilities.
Works with any open-weight model family, and stays current as the open-source model ecosystem evolves.
| Standing up an open-weight model, the old way | On Spacebus |
|---|---|
| Manually provision and configure GPU clusters | Auto-configured GPU allocation, calculated for you |
| Hand-tune parallelism and precision settings | Determined automatically from the model's architecture |
| Separate infrastructure requests for training vs. serving | One system, one flow, for both |
| Share capacity with other tenants | Dedicated, isolated capacity at every site |
| Weeks from decision to production | Live in a single deployment action |
Bring an open-weight model — or your own fine-tuned version — and see it live on dedicated capacity.
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