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)
training · inference · agents
One platform to operate the full AI lifecycle
You keep control of your infrastructure, policies, and data.
Nucleaton™ manages your GPU cluster operations and schedules compute for training, inference and agents.
Large-scale training & inference
Run multi-node training and production inference on the same cluster.
Automated fault management
Automatically isolate faulty nodes using the most advanced real-time telemetry. Reduce job failures and wasted compute hours.
Dynamic resource allocation
Reallocate compute between training and inference to reduce GPU idle time.
“C-Gen.AI dynamically allocates idle compute to inference workloads, so AI resources are continuously utilized rather than sitting idle.”
Dr. Sami KamaFounder & CEO, C-Gen.AIThe cost of idle GPUs
<50%
GPU capacity is used on many AI clusters between training runs.
You still pay for the idle capacity.
Reclaim idle GPU time and turn it into capacity or revenue.
GPU utilization, before and with Nucleaton™. Before: training runs at 70%, drops to 30%, stops, then runs at 45%, leaving 30%, 70%, 100% and 55% of GPUs idle. With Nucleaton™: training continues at 45% and inference fills the other 55%; training rises to 70% and inference yields to 30%; training pauses and inference takes the full cluster at 100%; training returns at 70% with inference at 30%; then training drops to 15% and inference takes 85%. Training plus inference never exceeds 100%.
- Training GPU utilization (avg)
- Inference GPU utilization (avg)
- Idle GPUs
- AI Startups
Reduce time to inference
Save seed capital on infrastructure engineering. Launch AI models faster.
- Data Centers
Maximize GPU monetization
Turn idle cluster compute into revenue with multi-tenant AI-as-a-Service.
- Enterprises
Private cloud sovereignty
Run secure, on-premises AI with built-in audit logs and cost attribution.
Led by a world-class team
We’ve designed, built and run some of the largest AI training and inference systems in production.
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.
Our platform is designed for immediate impact. You can move from bare-metal hardware to a production-ready AI cluster in minutes, not days. Nucleaton™ automates the manual configuration typically required for high-performance compute environments.
The platform is purpose-built for high-demand Large-Scale Training and Real-Time Inference. It is optimized for GPU-heavy workloads that require sophisticated fault management, high-throughput data handling, and dynamic resource allocation.
Security is built into our orchestration layer. C-Gen.AI enables Private Cloud Sovereignty, allowing enterprises to keep AI workloads secure and private within their own VPC or on-premise hardware, ensuring full data residency and compliance without vendor lock-in.
Yes. Our Dynamic Resource Switching repurposes idle training compute for inference, maximizing GPU utilization and transforming unpredictable infrastructure spend into a predictable operational cost.
Maximize ROI on
AI infrastructure
Stop managing infrastructure and start scaling.
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Blog
Insights on AI infrastructure: the real cost of complexity, the strategy behind scaling, and what it takes to keep up with the models.
Why AI infrastructure orchestration outperforms DIY models
How unified AI orchestration enables headcount independence and optimized GPU utilization for production-grade workloads.
Read more : Why AI infrastructure orchestration outperforms DIY modelsThe cost of complexity in AI that no one talks about
AI holds enormous promise — from powering customer insights to enabling predictive maintenance. But complexity has a price.
Read more : The cost of complexity in AI that no one talks aboutAI infrastructure must evolve as fast as the models it supports
The problem isn’t just technical. It’s strategic. If infrastructure can’t keep up, every model improvement is met with delay.
Read more : AI infrastructure must evolve as fast as the models it supports