Training, fine-tuning and online inference run on one scheduling layer, so a model developed in a pilot can be promoted to production without rebuilding the infrastructure. Capacity is allocated by agreed priority, and utilisation and cost per job are reported back to the customer console.
Compute capacity reserved for industry AI workloads rather than general-purpose hosting. Training, fine-tuning and online inference run on the same scheduling layer, so a model developed in a pilot can be promoted to production without re-plumbing the infrastructure.
Multi-GPU nodes for distributed training runs, with checkpointing and resume so long jobs survive interruptions.
Dedicated queues for LoRA and SFT jobs, sized to the dataset rather than to a full pre-training budget.
Elastic scaling for latency-sensitive endpoints, with autoscaling rules set per scenario and per customer.
Scheduled throughput for document batches, imagery review and periodic reporting at lower unit cost.
Job priority by project, with pre-emption rules agreed in advance so production traffic is protected.
GPU utilisation, queue depth and cost per job reported back to the customer console, not kept internal.
Where data cannot leave customer premises, the same scheduling layer runs on customer-side hardware.
Compute connects directly to Model Hub, Agent Cloud and Token Cloud, so capacity, capability and billing stay aligned.
Each tier is scheduled differently because the workloads behave differently. Inference is measured by latency, training by throughput, and batch by unit cost.
Single and dual accelerator nodes serving latency-sensitive endpoints, scaled by request rate and queue depth.
Capacity sized for LoRA and supervised fine-tuning jobs on industry datasets, queued by project priority.
Distributed training runs for industry models, with inter-node communication tuned for long jobs.
Scheduled capacity for large document batches, imagery review and periodic reporting at lower unit cost.
Jobs submitted through API or console enter a priority queue. Priority and pre-emption rules are agreed per customer, so production inference is never displaced by a training run.
Long training and fine-tuning jobs write checkpoints at defined intervals, so an interruption costs the last interval rather than the whole run.
Replica counts follow request rate and queue depth, with bounds set per endpoint so a traffic spike does not consume the whole allocation.
Accelerator utilisation, memory pressure, queue depth and cost per job are reported to the customer console rather than kept internal.
Runtime images, framework versions and model artefacts are versioned, so a result can be reproduced against the same environment.
Where data cannot leave customer premises, the same scheduling and reporting layer runs on customer-side hardware under a private arrangement.
Where documents, imagery or operational records cannot leave customer premises, the same scheduling and reporting layer runs on customer-side hardware. Capacity planning, model selection and access rules are agreed together rather than in separate conversations.
Capacity connects directly to domain models, so a model promoted from pilot to production keeps the same serving path.
View Model HubFine-tuning engagements run on the adaptation queue, with evaluation results reported per scenario.
View Fine-Tuning ServiceCompute consumption is attributed to the same project and customer records used for token billing.
View Token CloudShare the origin, destination, cargo type and target schedule. Our team will review the details and reply with a proposed arrangement.