GPU orchestration & cluster management
GPU orchestration decides which workload runs on which GPU, when, and for how long. Done well, a team shares expensive accelerators across training, fine-tuning and inference without jobs waiting for hours or cards sitting idle. Athryx Tech helps AI teams set up and manage that layer, working from Bucharest with clients in Europe and beyond.
Most GPU problems are management problems. Clusters grow one project at a time, nobody can see utilisation, inference competes with training for the same cards and the cloud bill arrives as a surprise. We bring structure: scheduling and quotas, autoscaling for inference, monitoring that shows where GPU time goes, and runbooks your team can operate.
We work with containerised workloads on Kubernetes and the GPU cloud platforms around NVIDIA's stack. As an NVIDIA Inception member with hands-on DGX Cloud Lepton exposure through the CEE Partners network, we are used to connecting models to the infrastructure and applications that depend on them.
This service is about running and managing GPU capacity. If you are still choosing hardware, providers or a deployment architecture, start with our GPU infrastructure service.
Problems we solve
- Training jobs queueing for hours while other GPUs sit idle
- Inference and training fighting over the same cards
- No visibility into GPU utilisation or cost per team, project or model
- Ad-hoc scheduling that only one engineer understands
Who this is for
- AI teams sharing a GPU cluster across projects or departments
- Startups scaling inference for an AI product
- Enterprises running hybrid cloud and on-premise GPU capacity
- Platform teams that want GPU workloads to behave like the rest of their infrastructure
What we deliver
- GPU scheduling, queues and quotas on Kubernetes
- GPU sharing strategies for mixed training and inference workloads
- Autoscaling for model serving based on real demand
- GPU utilisation and cost monitoring per team, project or model
- Containerised training and inference pipelines with CI/CD
- Hybrid and multi-cloud GPU capacity management
- Runbooks, alerts and handover to your platform team
Technologies & stack
- NVIDIA GPUs and CUDA
- Kubernetes and container orchestration
- Docker and CI/CD for ML workloads
- PyTorch, ONNX and model serving frameworks
- DGX Cloud Lepton, AWS, GCP and specialised GPU clouds
- Metrics, dashboards and alerting for GPU workloads
How we work
- 01Audit current workloads, utilisation and spend
- 02Design scheduling, sharing and scaling rules around your priorities
- 03Roll out in stages, starting with the workloads that waste the most GPU time
- 04Hand over dashboards and runbooks, then iterate with your team
Selected reference
NVIDIA Inception programme member. Hands-on contribution to DGX Cloud Lepton via the CEE Partners network, a GPU cloud platform connecting developers to global compute for inference, training, and AI deployment. Participation in GTC Paris 2025 with workshops on Agentic AI.
Frequently asked questions
What is GPU orchestration?
GPU orchestration is the automated scheduling and management of GPU resources across workloads. It decides which jobs run on which GPUs, shares capacity between teams and scales inference up or down with demand.
What is the difference between GPU infrastructure and GPU orchestration?
Infrastructure is the hardware, cloud providers and architecture you choose. Orchestration is how you run and share that capacity day to day. We help with both.
Do you work with on-premise GPUs or only the cloud?
Both. Many teams run a mix of owned hardware and cloud GPU capacity, and orchestration is what keeps that mix efficient.
Do you work with clients outside Romania?
Yes. We work remotely in English with teams in Europe and the US. GPU infrastructure work is well suited to remote delivery.
Related services
Getting less out of your GPUs than you pay for?
Tell us how your cluster is used today. We will point out the quickest wins in utilisation and cost, and what a managed setup would look like.
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