AI Inference Server with Tensor Core GPUs

Deploy AI models at scale with our Tensor Core GPU server—NVIDIA A100, 512 GB RAM, 8× 10 GbE, NVMe. Fast UAE delivery. Order today!

Overview

Fuel real-time AI inference with our dedicated server powered by NVIDIA Tensor Core GPUs. Equipped with dual Intel® Xeon® CPUs, up to four NVIDIA A100 GPUs, and 512 GB DDR4 ECC RAM, it delivers sub-millisecond latency for vision, speech, and recommendation engines. Scalable, energy-efficient, and built for data-center deployment.

Product Description

This AI Inference Server integrates four NVIDIA A100 Tensor Core GPUs in a 2U chassis, delivering up to 312 TeraFLOPS of mixed-precision throughput. Dual Intel® Xeon® Scalable processors handle I/O and preprocessing tasks, while 512 GB DDR4 ECC memory and 8 × 1 TB NVMe drives ensure fast data access. Eight 10 GbE ports provide high-speed connectivity for model serving and data ingestion. Remote management via IPMI and iDRAC9 simplifies updates and monitoring. Ideal for deploying TensorFlow, PyTorch, ONNX, and Triton workloads at scale.

Key Features

• Up to 4 × NVIDIA A100 40 GB PCIe GPUs

• Dual Intel® Xeon® Scalable CPUs (24 cores each)

• 512 GB DDR4 ECC RAM

• 8 × 1 TB NVMe hot-swap SSDs

• 8 × 10 GbE RJ-45 network ports

• IPMI & iDRAC9 remote management

• Redundant 1600 W Titanium PSUs

Specifications

• Form Factor: 2U rack chassis

• GPUs: 4 × NVIDIA A100 Tensor Core (40 GB each)

• CPUs: 2 × Intel® Xeon® Scalable (up to 205 W each)

• Memory: 16 × DDR4 ECC DIMM slots, up to 512 GB

• Storage: 8 × 1 TB NVMe SSD, RAID 0/1/5/10

• Networking: 8 × 10 GbE RJ-45 ports

• Power: 2 × 1600 W Titanium PSUs (redundant)

• Management: IPMI 2.0, iDRAC9, SNMP

Supported OS / Applications / Industries

• OS: Ubuntu Server, CentOS, RHEL, VMware ESXi

• Frameworks: TensorFlow, PyTorch, MXNet, ONNX, Triton

• Industries: AI services, autonomous vehicles, healthcare imaging, finance

Benefits & Compatibility

• Scales deep-learning inference with Tensor Cores

• Lowers latency for real-time AI applications

• Fits standard 19″ data-center racks

• Integrates with Kubernetes and ML ops pipelines

Purpose of Use

Deploy production AI inference workloads—computer vision, natural language processing, recommendation systems, and time-series forecasting—at enterprise scale.

How to Use

1. Rack-mount the server in a 19″ bay.

2. Connect to power and 10 GbE network.

3. Use iDRAC9 for BIOS, firmware, and GPU firmware updates.

4. Install your AI stack (CUDA, cuDNN, frameworks) and deploy models via Triton or custom APIs.

Packaging / Weight / Dimensions

• Dimensions: 89 mm H × 438 mm W × 800 mm D

• Weight: ~35 kg (fully loaded)

• Packaging: Anti-static wrap, foam-lined crate, quick-start guide

Warranty & FAQs

• Warranty: 3-Year Limited Hardware Warranty

FAQ:

• Can I mix GPU models? Yes, mix A100 and A40 in supported slots.

• Is NVMe RAID supported? Supports RAID 0/1/5/10 via onboard controller.

• How to monitor GPU health? Use iDRAC9 dashboard or NVIDIA DCGM.

Performance, Quality, Durability & Reliability

Certified for 24/7 AI workloads, this server uses enterprise-grade components, redundant cooling zones, and power supplies to ensure consistent high-performance and uptime.

Best Price Guarantee

Found a lower UAE price? We’ll match it. Shop confidently with NEOTECH’s price-match promise.

Shop Today & Receive Your Delivery Across the UAE!

Order now for secure, trackable delivery across all Emirates. We pack and ship on your timeline to meet your project deadline.

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After Sales Support

Our ML-specialist engineers provide setup, optimization, and troubleshooting support via phone or remote session.

Get in Touch

Need custom GPU counts or ML-ops integration? Contact NEOTECH’s AI infrastructure team for expert advice and tailored quotes.

Stock Availability

Please confirm stock status before ordering to secure your delivery slot and avoid project delays.

Disclaimer

Specifications and pricing are subject to change without notice. Images are illustrative only. Verify all details with our sales team before purchase.