ASUS
SKU: PRIME-RTX5060TI-O8G
Overview
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Overview
Questions about this product? Free pre-sales support from a senior specialist — product questions, compatibility checks, BOM quotes, price confirmation — typically answered within one business day. Need camera placement or system design work? Engineering time is $175 per hour (qty 1 = 1 hour). Hardware buyers get up to one hour ($175) credited back on their order.
The ASUS PRIME-RTX5060TI-8G is a GeForce RTX 5060 Ti graphics card built around NVIDIA's Blackwell architecture, pairing 4608 CUDA cores with 8GB of GDDR7 memory on a 128-bit bus — a configuration that delivers strong throughput for AI inference workloads, video analytics decoding, and multi-display operator stations. At a 2.5-slot profile and 16.00 x 3.50 x 9.50 in (L x W x H), it carries an SFF-Ready designation, meaning it fits compact workstations and edge servers where a full 3-slot card simply won't clear the chassis. If your deployment involves GPU-accelerated graphics cards in space-constrained environments — think compact video analytics servers or small-form-factor workstations running VMS client software — this is the card worth evaluating.
The PRIME-RTX5060TI-8G connects via PCIe 5.0 x16 and is backward-compatible with PCIe 4.0 and 3.0 motherboards. The Ethernet interface attribute in the source data reflects platform-level connectivity rather than a dedicated GPU NIC — confirm your host system's network configuration separately. With support for up to four displays simultaneously and a maximum resolution of 7680×4320, the card integrates cleanly with NVR workstation builds and operator consoles running modern VMS clients. CUDA support makes it compatible with NVIDIA-accelerated AI inference tools. Verify your chassis accommodates a 16.00-inch card length and 2.5 slots of clearance before installing. The SFF-Ready designation is a fitment standard, not a chassis-specific guarantee — cross-reference your enclosure's GPU clearance spec. For builds requiring a PoE switch or additional networking alongside this GPU in a compact chassis, confirm slot spacing with 2.5 slots consumed by this card.
Q: What is the physical size of the PRIME-RTX5060TI-8G, and will it fit in a small form factor case?
A: The card measures 16.00 x 3.50 x 9.50 inches (L x W x H) and occupies 2.5 expansion slots. It carries an SFF-Ready designation, but you must verify your specific chassis supports a 16-inch card length and has 2.5 slots of clearance at the GPU bay.
Q: Does the PRIME-RTX5060TI-8G support multi-GPU or NVLink configurations?
A: No. Parallel processing technology (NVLink/multi-GPU) is listed as not supported on this SKU. It is a single-card deployment only.
Q: How many monitors can I connect, and what are the output specs?
A: The card supports up to four simultaneous displays via one HDMI 2.1b port and three DisplayPort 2.1b ports. Maximum resolution is 7680×4320 (8K).
Q: Is CUDA supported on the PRIME-RTX5060TI-8G, and what AI/compute workloads does that enable?
A: Yes, CUDA is confirmed supported. The card's 4608 CUDA cores are compatible with NVIDIA-accelerated frameworks including TensorRT, CUDA-accelerated OpenCV, and NVIDIA DeepStream for AI inference and video analytics workloads.
Q: What PCIe version does this card require, and is it backward-compatible?
A: The card uses a PCIe 5.0 interface and is backward-compatible with PCIe 4.0 and 3.0 slots. Full PCIe 5.0 bandwidth is only realized on a PCIe 5.0-capable host platform.
Q: What is the memory configuration and bandwidth on the PRIME-RTX5060TI-8G?
A: The card has 8GB of GDDR7 memory on a 128-bit bus running at 28 Gbit/s. This is sufficient for most mid-range AI inference and multi-stream video decode workloads, but VRAM-intensive models exceeding 8GB will require a higher-memory variant.

The PRIME-RTX5060TI-8G is one of the few Blackwell-generation cards that hits a 2.5-slot profile without sacrificing the 4608 CUDA cores you actually need for GPU-accelerated analytics. In compact deployments — think edge AI servers or small-form-factor workstations adjacent to camera infrastructure — that slot count matters as much as the GPU spec itself.
Technical Highlights:
Deployment Considerations:
Best fit for a compact edge AI workstation running GPU-accelerated VMS analytics or a four-display operator console station where chassis depth is constrained and a full 3-slot card isn't an option.
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