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C-Gen.AI

Own the full AI loop.

Run training, production inference, and agents on infrastructure you control. Nucleaton™ brings compute, workloads, and production data under one resource and governance model, so capacity can move where it is needed and selected data created in production can remain available for evaluation and future training.

Train. Deploy. Learn from production. Train again.

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  1. Training

    Jobsqueuescheckpoints

    compute + storage

    Deploy

  2. Inference

    Endpointstrafficagents

    live serving

    Learn

  3. Data + Improvement

    Tracesfeedbackevaluation

    under your governance

    Train again

One Nucleaton™-controlled environment.

One resource policy • one data boundary • one operating model. Workloads remain distinct; infrastructure and governance share one foundation.

One AI Lifecycle

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.

Three separate systems

  • Training

    • compute + storage
    • idle headroom

  • Inference + Agents

    • compute + storage
    • idle headroom

  • Data + Improvement

    • compute + storage
    • idle headroom

Capacity is fixed per system, and each one also carries its own operations and data path.

Pooled with Nucleaton™

One Nucleaton™ Environment

  • Training

  • Inference + Agents

  • Data + Improvement

  • pooled compute + storage
  • shared headroom

All three share compute, storage and headroom under one policy, one operating model and one data boundary.

One environment. One resource policy. One data boundary.

Capacity Economics

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.

Separate pools

  • Training Pool200 GPUs · 160 active · 40 headroom
  • Inference Pool100 GPUs · 55 active · 45 headroom
  • Agents / Evaluation40 GPUs · 28 active · 12 headroom

Each pool is sized for its own peak. Three sets of unused headroom: 97 GPUs held in reserve.

Prioritized shared pool

Same Work, One Pool, One Headroom

Separate pools340 GPUs · 97 headroom
One shared pool278 GPUs · 35 headroom
  • Training
  • Inference
  • Agents / evaluation
  • Headroom

Illustrative figures. Actual headroom depends on workload timing and your priorities.

Peaks rarely coincide, so one shared headroom covers them all: 35 GPUs held in reserve, and 62 fewer GPUs to buy.

Reuse headroom instead of duplicating it.

Customer-Defined Policy

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.

Guaranteed capacity:
Never consumed by opportunistic workloads.
Available capacity:
Eligible training, inference, agents, evaluation, or other workloads can use it.
Return on demand:
Reclaim immediately, or stop new requests and drain active inference before releasing capacity.

One example of how a policy plays out

This is one example. How much is protected, which workloads come first and how capacity is reclaimed are all set by your policy.

One pool · 100 GPUsProtected 30 · Flexible 70
  1. Normal OperationFlexible capacity is fully used. Protected capacity stays held, even when idle.

    Guaranteed 18Held 12Training 30Inference 30Agents 10

  2. Priority Training ArrivesTraining needs 20 more GPUs. Inference stops new requests and drains 20.

    Guaranteed 18Held 12Training 30Draining 20Inf. 10Agents 10

  3. Capacity ReturnedTraining runs on the reclaimed GPUs. The protected zone never moved.

    Guaranteed 18Held 12Training 50Inf. 10Agents 10

  • Protected, in use
  • Protected, held idle
  • Training
  • Inference
  • Agents
  • Draining, returning to the pool

Your team defines the architecture and policies. Nucleaton™ automates their execution.

Privacy + Sovereignty + Data

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.

  • Privacy
  • Sovereignty
  • Institutional data ownership
  • Continuous improvement

Customer-controlled boundary

  • Models + Agents

    InferencetoolsRAG

  • Production Data

    Tracesfeedbackoutcomes

  • Storage + Keys

    Customer controlled

Models, production data, storage and keys stay inside infrastructure you control.

Governed improvement path

  1. Retain
  2. Filter / redact
  3. Evaluate
  4. Curate
  5. Future training

You decide what is kept and what is used for training.

Your production data stays yours, and keeps improving your models.

Compound the Intelligence

From training to production, and back again.

  1. Train
  2. Deploy
  3. Inference+ Agents
  4. Interactions+ Feedback
  5. Evaluate+ Curate
  6. Improve+ Post-train
  7. Deploy

What are you operating?

  • Enterprise / AI Infrastructure

    Make clusters support the full AI lifecycle.
    Training, inference, agents, users, storage, health, and resource policies under one infrastructure foundation.

    Explore enterprise infrastructure

  • Data Center / GPU Cloud

    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.

    For data center operators

Resource Authority

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.

Starting fresh? Nucleaton™ can build the environment.
Already have Slurm? Existing clusters can be integrated through an assisted brownfield deployment.

Slurm

  • CPU/GPU allocation
  • Multi-node scheduling
  • Queues and priorities
  • Jobs
  • Accounting primitives

Nucleaton™

  • Cluster lifecycle
  • Demand-driven inference scaling
  • Request routing / endpoints
  • Protected/flexible pools
  • Immediate or graceful reclaim
  • Users / storage / health / remediation
  • Optional K8s overlay
Resource foundation
Nucleaton™deploys, runs and extends Slurm

Inference scalingRouting + endpointsPriority + reclaimUsers + storageHealth + remediation

Kubernetes overlay
Optional

For workloads that need Kubernetes semantics, on the same physical fleet.

inference endpointsagent hostingmodel serving

Slurm resource authority
Foundation

CPU/GPU allocationmulti-node jobsqueuesprioritiesaccounting

Platform Depth

Not another autoscaler.
The operating layer for the cluster.

Nucleaton™ operating layer

  1. Workloads

    TrainingInferenceAgentsEvaluationCPU/Data services

  2. Operations

    AutoscaleRoutePrioritizeReclaim/DrainHealth/Remediate

  3. Control

    UsersProjectsQuotasAccessPolicy

  4. Cluster

    SlurmStorageNetworkGPU stackOptional K8s

  5. Infrastructure

    NVIDIAAMDCPUCloudData Center

Engineering Depth

The automation goes all the way down.

  • ~25 minTypical greenfield deployment from paired account + finalized infrastructure choices to SSH-ready cluster.
  • ~30 secInference endpoint configuration after model/runtime artifacts are prepared.
  • SecondsImmediate reclaim of supplemental inference capacity. Graceful active-request draining can be configured.

Deployment time subject to infrastructure availability. 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 to stop making the infrastructure team rebuild the same control-plane machinery.

Led by a world-class team.

Dr. Sami Kama

Dr. Sami Kama

Founder & CEO
  • 17 years at CERN
  • Inventor, TensorFlow-TensorRT Integration (NVIDIA)
  • Inventor and Architect, AWS SageMaker
  • Inventor and Architect, AWS SageMaker HyperPod
  • Creator, SageMaker Profiler (world’s first distributed AI profiler)
Richard Vencu

Richard Vencu

Head of Engineering
  • Former Head of HPC & MLOps, Stability AI
  • Managed one of the world’s largest AI training clusters
  • Co-Founder & Engineering Lead, LAION

FAQs

Can’t find your answer? Contact us.

C-Gen.AI is a turnkey AI infrastructure orchestration platform. Unlike a standard cloud provider, we provide the software layer (Nucleaton™) that automates the deployment, management, and monetization of GPU clusters across any environment—whether on-premise, private cloud, or bare-metal.

Keep the compute, the data, and the intelligence loop under your control.

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At C-Gen.AI, we see ourselves as the connective tissue between model innovation and operational delivery for startups, data centers, and enterprises.

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