GPU clusters and MLOps: your model from training to production
GPU clusters for model training and inference, a ready-made MLOps environment and on-demand scaling infrastructure; run your AI workloads in the cloud while keeping data in Turkey.
GPUs are expensive; an idle GPU is the most expensive of all
In AI projects GPUs are both scarce and expensive. Not finding capacity when you look for it for training, and leaving an expensive GPU idle after a finished experiment, both burn through the budget. The need is to be able to spin up capacity exactly when required and shut it down when the job is done.
Multi-GPU nodes are spun up for training and shut down hourly when the job is done.
Heavy training runs in batches while the low-latency inference service scales separately.
Experiment tracking, model registry and deployment pipeline come ready.
Training data is kept on high-throughput storage within Turkey.
Recommended architecture
A typical AI (MLOps) architecture that separates data preparation, training and inference; GPU capacity is spun up and down on demand.
What AI teams say
Frequently asked questions about AI cloud infrastructure
Multi-GPU training nodes are spun up within minutes and shut down hourly when the job is done. This way you spin up capacity exactly when needed and do not leave an expensive GPU idle when the experiment is over.
Training is a heavy, batch and time-tolerant workload; inference is continuous and demands low latency. By scaling the two separately, we spin up large GPUs temporarily for training and auto-scale the inference service on demand.
Experiment tracking, model registry, versioning and a deployment (CI/CD) pipeline come set up; you move the model from training to production in a traceable and reproducible way. We also integrate with your existing toolset.
Training data is kept on high-throughput storage and Tier III+ infrastructure in Turkey; data does not leave the country. This matters for projects with KVKK and data sovereignty requirements.
Common deep learning frameworks and container-based workflows are supported; you can customize the environment with your own images. GPU drivers and acceleration libraries come ready.
Yes. The model you train is published behind an API gateway as an auto-scaling inference service; capacity grows as request load rises and shrinks as it falls. Access and usage are monitored with Logchase.
GPUs are billed hourly and spin up and down per job. A reservation discount is recommended for continuously running inference workloads, and a pay-as-you-go model for experimental training. You manage spend proactively with budget alerts.
Expensive GPU servers that were kept always on were moved to multi-GPU training nodes that spin up and down per job. The inference service was separated out and auto-scaled; both the capacity bottleneck and the idle-GPU problem were solved.
Talk to a team that knows your sector
A solution architect who has run projects in that sector joins the meeting. In the first meeting we produce an architecture draft and a cost range.
Schedule a meetingWe build solutions for every sector and need
The following are the architectures we build most often. Even if your need is not on the list, our solution architects design an end-to-end architecture tailored to your workload — at any scale.