ASUS Dual GeForce RTX 4060 Ti OC 8GB GDDR6

Brand: ASUS | Category: GPUs

SKU: DUAL-RTX4060TI-O8G | Part #: DUAL-RTX4060TI-O8G | MPN: DUAL-RTX4060TI-O8G

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The ASUS Dual GeForce RTX 4060 Ti OC (part number DUAL-RTX4060TI-O8G) is a compact, factory-overclocked GPU designed for data centre inference workloads, AI model serving, and professional compute tasks where physical space and power efficiency are critical constraints. Built on NVIDIA's Ada Lovelace architecture (AD106 chip), this card delivers 4,352 CUDA cores, 136 Tensor cores, and 34 RT cores paired with 8GB of GDDR6 memory across a 128-bit bus. The factory overclock to 2,535 MHz ensures consistent performance for real-time inference, batch processing, and machine learning applications, while the 160W TDP allows deployment in power-constrained environments such as edge compute nodes, small-form-factor servers, and distributed inference clusters. Its dual-fan, sub-240mm design makes it ideal for organisations needing GPU acceleration without the thermal and spatial overhead of larger, multi-slot alternatives.

Key Features

• Ada Lovelace GPU architecture (AD106): Fourth-generation NVIDIA CUDA compute platform with improved tensor performance and memory bandwidth efficiency for AI inference and HPC workloads
• 4,352 CUDA cores and 136 Tensor cores: Sufficient parallel compute for real-time neural network inference, model serving, and batch-processing tasks at enterprise scale
• 8GB GDDR6 memory with 128-bit bus: Supports mid-size language model inference, computer vision pipelines, and multi-model serving without excessive memory arbitrage
• Factory overclock to 2,535 MHz: Pre-tuned boost frequency delivers predictable performance out of the box, eliminating manual OC configuration overhead in production deployments
• 160W TDP: Low thermal envelope reduces PSU sizing requirements and cooling infrastructure costs in edge, colocation, and small-footprint rack deployments
• Compact dual-fan design, sub-240mm length: Fits into single-slot and dual-slot server enclosures, ITX chassis, and integrated systems where standard triple-slot cards are mechanically incompatible
• PCIe 4.0 x16 interface: Full-bandwidth connectivity to modern server platforms; backwards compatible with PCIe 3.0 and 5.0 systems
• 3x DisplayPort 1.4a + 1x HDMI 2.1a output: Flexible display connectivity for headless inference servers or multi-monitor remote management scenarios

Typical Use Cases

• Real-time AI inference at the edge: Deploy LLM token generation, vision transformers, and recommendation engines on distributed inference nodes across GCC regions with minimal latency
• Batch model serving in containerised environments: Kubernetes-based inference clusters running quantised models (INT8, TF32) with sub-240mm form-factor constraints in hyperconverged or compact rack infrastructure
• Computer vision and object detection pipelines: Concurrent YOLO, ResNet, and custom CNN inference across multiple streams on research and production clusters
• Machine learning training acceleration (small-scale): Fine-tuning of LoRA adapters, small embedding models, and transfer learning workflows in laboratory and prototyping environments
• Scientific computing and data analytics: CUDA-accelerated molecular simulation, finite-element analysis, and numerical computing in compact server form factors
• Multi-GPU distributed inference systems: Stack multiple Dual RTX 4060 Ti cards via NVLink-free CPU interconnect for horizontally scaled inference clusters with shared CPU-GPU memory coherence

The ASUS Dual GeForce RTX 4060 Ti OC stands out for its combination of proven Ada compute density, minimal physical footprint, and verified factory clocks—making it one of the few single-slot 8GB GPU options suitable for production inference deployments in space-constrained data centres. Omnixon Global stocks this SKU and can source additional units for UAE, Saudi, and broader GCC enterprises. Contact our technical sales team to discuss integration into your inference cluster, request a formal quote, or arrange evaluation hardware for your AI workload.

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