ASUS Dual GeForce RTX 5060 Ti OC 16GB GDDR7

Brand: ASUS | Category: GPUs

SKU: DUAL-RTX5060TI-O16G | Part #: DUAL-RTX5060TI-O16G | MPN: DUAL-RTX5060TI-O16G

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Memory bandwidth and capacity remain the primary bottleneck in enterprise AI inference and training workloads. The ASUS Dual GeForce RTX 5060 Ti OC 16GB GDDR7 (part number DUAL-RTX5060TI-O16G) addresses this constraint directly with 16GB of GDDR7 memory paired to a 128-bit bus architecture. This configuration delivers 576 GB/s of memory bandwidth—a specification that matters when you're loading language models, fine-tuning vision transformers, or running batch inference across multiple concurrent sessions. The RTX 5060 Ti sits at a capacity and bandwidth sweet spot for enterprises and research labs that need more than a single-slot consumer card can provide, but operate within power and rack-density constraints that rule out larger, multi-slot designs.

Built on NVIDIA's Blackwell architecture with the GB206 GPU chip, the ASUS DUAL-RTX5060TI-O16G delivers 4,608 CUDA cores, 36 RT cores, and 144 Tensor cores. The 759 AI TOPS figure reflects the card's throughput for mixed-precision workloads common in inference pipelines—quantized models, FP8 operations, and tensor matrix multiplications that define real-world deployment scenarios rather than theoretical peak performance. That 180W TDP is pragmatic for data-centre power budgeting; it means you can install multiple cards in a single 2U or 3U host without triggering PSU scaling nightmares or requiring dedicated power distribution redesigns. Clock speeds reach 2662 MHz at boost, though ASUS's factory-overclocked binning on this model provides stable headroom above reference specifications without requiring manual tuning.

ASUS has configured the DUAL-RTX5060TI-O16G with dual-fan thermal design and a compact dual-slot form factor. That matters in practice. AI infrastructure teams and systems architects building out GPU clusters across GCC data centres often inherit retrofit constraints—existing server chassis, shared cooling loops, and power rails designed for older generations. The 128-bit bus and 16GB GDDR7 configuration keeps the footprint and power envelope predictable, while the PCIe Gen5 x8 interface ensures full bandwidth utilization even in dense server builds where x4 lanes would bottleneck throughput. Display outputs—one HDMI 2.1b and three DisplayPort 2.1b connectors—support multi-monitor or high-resolution visualization workstations without requiring external adapters, though in data-centre roles these typically remain unpopulated.

This GPU works efficiently in both dense inference clusters and smaller research environments. A data-centre infrastructure team deploying 4–8 units across multiple hosts benefits from the low power consumption, which simplifies cooling and electrical supply calculations. The same card fits equally well in a university AI lab or an enterprise ML team's on-premise training cluster. Sysadmins appreciate the stable thermal profile and straightforward driver support, while ML infrastructure engineers value the Tensor core density and memory bandwidth-to-cost ratio for model serving. The DUAL-RTX5060TI-O16G is not a single-workload specialist; it trades some peak performance for versatility, which is exactly what most enterprises actually need across production inference, fine-tuning, and validation pipelines running in parallel.

Omnixon Global maintains stock of the ASUS DUAL-RTX5060TI-O16G (part number DUAL-RTX5060TI-O16G) across our regional distribution centres in Dubai and beyond, supporting rapid deployment to GCC enterprises, Asia-Pacific data-centre operators, and European AI infrastructure teams. Our technical team has field experience with multi-card configurations, thermal integration, and driver optimization across diverse host platforms. Whether you are planning a single-unit evaluation or a large cluster acquisition, we support your procurement with technical validation, configuration review, and supply-chain reliability. Contact our B2B sales team to discuss your AI infrastructure roadmap and request a quote.

Performance Specifications & Deployment Profile

• 16GB GDDR7 memory with 576 GB/s bandwidth on 128-bit bus—primary constraint for batch inference and model fine-tuning
• 4,608 CUDA cores and 144 Tensor cores deliver 759 AI TOPS for mixed-precision workloads common in production inference
• 180W TDP supports dense multi-card configurations without power infrastructure redesign
• PCIe Gen5 x8 interface ensures full bandwidth utilization in server and workstation host platforms
• Factory-overclocked to 2662 MHz boost clock; dual-fan dual-slot design for compact, thermally stable deployment
• HDMI 2.1b and triple DisplayPort 2.1b outputs support visualization and multi-display workstations
• Blackwell architecture (GB206 chip) and 36 RT cores enable real-time ray-tracing and advanced rendering alongside AI workloads