NVIDIA certified GPU configuration
Pods are built on NVIDIA H100, A100 and L40S GPUs with NVLink interconnect, certified hardware and enterprise support included.
emtech delivers fully managed NVIDIA GPU compute pods for UAE enterprises that cannot use public cloud for AI workloads. Pre configured, rack mounted and operational within 2 to 4 weeks, with emtech managing the hardware, software and optimisation so your data science team just runs models.
Keep models, datasets and inference traffic inside your facility while emtech manages the compute platform end to end.
Faster AI training on H100 versus previous generation A100 GPU in many transformer workloads.
Growth in UAE on premise AI compute demand from 2022 to 2024.
From order to operational AI compute pod when the data centre is ready.
UAE data residency with no training data leaving your facility.
emtech designs GPU compute pods around NVIDIA acceleration, Kubernetes GPU scheduling, enterprise virtualisation, Red Hat platforms, MLOps tools, high speed storage and secure operations.
These definitions help business and technical leaders compare GPU servers, MLOps platforms and private AI architecture accurately.
A specialised processor with thousands of cores optimised for parallel AI computation.
Video RAM on GPU that determines the maximum model size that can run in memory. H100 and A100 can support 80GB configurations, while L40S supports 48GB.
NVIDIA high bandwidth GPU interconnect that enables multiple GPUs to act like one large memory and compute pool.
Ultra low latency network fabric used to connect multiple GPU servers for distributed AI training.
Machine Learning Operations, the CI/CD pipeline for AI models from development to production deployment.
Container orchestration that dynamically allocates GPU resources to AI workloads across teams.
Half precision formats that can double AI training throughput compared with FP32 where model accuracy allows.
A pre configured, rack mounted GPU server solution delivered as a managed appliance and operational in 2 to 4 weeks.
Pods are built on NVIDIA H100, A100 and L40S GPUs with NVLink interconnect, certified hardware and enterprise support included.
Pre installed MLflow, Kubeflow and JupyterHub give your data science team experiment tracking, pipelines and notebook access from day one.
NVMe all flash storage connected through InfiniBand helps eliminate GPU idle time while training jobs stream large datasets.
Dynamic GPU allocation across multiple teams with namespace isolation, resource quotas and priority queuing for enterprise AI workloads.
emtech monitors GPU temperature, utilisation, memory and job queues continuously with component replacement under SLA.
Network isolation, encrypted storage, role based access and audit logging support UAE sensitive data under NESA and PDPL expectations.
Designed as a sovereign AI workload gallery, each environment shows where GPU pods create immediate value without sending sensitive UAE data into public cloud GPU queues.
Train fraud detection and credit scoring models on UAE transaction data without public cloud exposure.
Run LLMs and AI models on classified government data with a full air gapped option.
Train clinical models on patient data that cannot leave the healthcare facility under ADHICS.
Train predictive maintenance models on SCADA data from UAE oil, gas and power infrastructure.
Train recommendation and demand forecasting models on proprietary retail transaction data.
Give UAE AI companies affordable GPU access without building data centre infrastructure from scratch.
We map model size, VRAM, training hours, inference needs and expected utilisation.
Rack space, 40 to 80 kW density, UPS, cooling and networking are validated.
GPU servers, storage, fabric and secure connectivity are installed and tested.
MLflow, Kubeflow, JupyterHub and GPU scheduling are configured for users.
emtech monitors, tunes and reports on utilisation, bottlenecks and capacity growth.
Whether you need H100 training, L40S inference, private LLM hosting or a managed MLOps platform, emtech will map the right compute, storage, power and operating model.
Build dedicated GPU capacity inside your environment, protect sensitive data and give your AI team infrastructure that is managed, optimised and ready to scale.
