Your AI lifecycle should not become three infrastructure silos.
Training, inference, and agents are often deployed as separate systems, each with its own compute, headroom, operations, and data path. Nucleaton brings those workloads under one resource and governance model.
One environment. One resource policy. One data boundary.
Stop duplicating headroom across AI workloads.
Peak training, production inference, and agent workloads each need room to grow. When they live in separate resource pools, each pool carries its own unused headroom. Nucleaton lets eligible capacity move between workloads according to the priorities you define.
Use already-committed compute before buying more compute elsewhere.
This is not free compute. Power, storage, networking, and operations still have cost. The advantage is reducing unnecessary duplication of committed capacity.
Some capacity is shared. Some is sacred.
You decide what must remain available, what can be used opportunistically, and how resources return when higher-priority work arrives. Nucleaton enforces those decisions continuously.
Never consumed by opportunistic workloads.
Eligible training, inference, agents, evaluation, or other workloads can use it.
Reclaim immediately, or stop new requests and drain active inference before releasing capacity.
Your team defines the architecture and policies. Nucleaton automates their execution.
Production AI creates an asset: the interaction history.
Agent traces, model responses, corrections, evaluations, and workflow outcomes can become valuable inputs to future evaluation and model improvement. Running the serving system inside infrastructure you control keeps selected production data within your AI environment and under your governance.
Customers control retention, access, filtering, redaction, and training eligibility. Do not imply automatic training on every interaction.
From training to production, and back again.
What are you operating?
Make one cluster support the full AI lifecycle.
Training, inference, agents, users, storage, health, and resource policies under one infrastructure foundation.
Turn physical GPU capacity into an operated AI service.
Connect tenant/service contracts to the physical fleet, improve eligible capacity utilization, and offer managed training, inference, and agent services.
Why Slurm at the foundation?
AI training needs deterministic access to scarce accelerators, multi-node coordination, queues, priorities, and accounting. Nucleaton uses Slurm as the base resource authority so training, inference, agents, and CPU workloads draw from one resource model.
- CPU/GPU allocation
- Multi-node scheduling
- Queues and priorities
- Jobs
- Accounting primitives
- Cluster lifecycle
- Demand-driven inference scaling
- Request routing / endpoints
- Protected/flexible pools
- Immediate or graceful reclaim
- Users / storage / health / remediation
- Optional K8s overlay
Starting fresh? Nucleaton can build the environment. Already have Slurm? Existing clusters can be integrated through an assisted brownfield deployment.
Not another autoscaler.
The operating layer around the cluster.
The automation goes all the way down.
Typical greenfield deployment from paired account + finalized infrastructure choices to SSH-ready cluster, subject to infrastructure availability.
Inference endpoint configuration after model/runtime artifacts are prepared.
Immediate reclaim of supplemental inference capacity. Graceful active-request draining can be configured.
Initial model download and container conversion are separate, model-dependent preparation steps.
Automate execution.
Keep architectural control.
Nucleaton automates work an infrastructure team would otherwise build and operate: cluster lifecycle, workload control, routing, scaling, monitoring, and resource enforcement. Your team still owns the architecture, priorities, access model, workload policies, and operational boundaries.
The goal is not to remove the infrastructure team. It is to stop making the team rebuild the same control-plane machinery.