Compute Infrastructure Built for AI Workloads.
From GPU servers to multi-node clusters, we plan, deploy and integrate infrastructure that fits your AI training and inference workloads.
AI workloads require compute, memory, networking and storage layers that are designed to work together. TURKZEN sizes infrastructure to your workload profile, from GPU server selection and cluster architecture to high-speed networking and data platforms. With Kubernetes-based GPU orchestration and a monitoring layer, we aim to make infrastructure shared, measurable and manageable. Responsibilities for hardware supply, OEM-authorized service and system integration are defined clearly in every project.
TURKZEN does not claim NVIDIA DGX reseller or warranty-service authorization. Our system integration services are defined separately from OEM-authorized support and warranty services.
Workload-driven sizing
GPU, memory and storage capacity are planned around your models and usage profile.
Balanced architecture
Compute, networking and storage are designed together to avoid bottlenecks.
Shared GPU resources
Kubernetes orchestration lets teams and projects use GPU capacity efficiently.
Visibility and monitoring
Utilisation, thermal and performance metrics are collected in a single monitoring layer.
AI Computing Infrastructure
AI Computing Infrastructure
GPU computing overview
GPUs execute the parallel matrix operations that AI models depend on with high efficiency. The right GPU choice is driven by model size, memory requirements and concurrency targets.
AI cluster architecture
An AI cluster consists of compute nodes, high-speed networking, shared storage and a management layer. We design these layers together in line with your scaling goals.
HPE and Dell server solutions
Solutions are designed around technologies including HPE and Dell server platforms. Platform choice reflects your existing estate, support model and workload requirements.
NVIDIA GPU technologies
NVIDIA GPUs and their supporting software components are widely used in enterprise AI infrastructure. We configure driver, container and monitoring components to fit the wider infrastructure.
Networking & storage
GPUs only perform well when data reaches them fast enough. We plan the network and storage layers to stay in balance with compute capacity.
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