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| Product Overview |
|---|
The PNY NVIDIA RTX 4000 Ada Generation 20GB small form factor graphics card offers a strong combination of performance and size. It's designed to fit into compact workstations while delivering the graphical and AI processing power professionals need for visualization, rendering, and compute-heavy tasks. This model fits well in environments where space is limited but demanding GPU capabilities are essential. |
| General Information | |
|---|---|
| Brand | PNY Technologies |
| Part Number | VCNRTX4000ADALP |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCI Express 4.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, Vulkan: 1.3, CUDA, Shader Model: 6.7, DirectCompute |
| Output Interface | DisplayPort |
| Memory | |
|---|---|
| Interface | GDDR6 |
| Memory Bus | 320-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Low Profile, Single Slot |
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, CE, FCC |
| Product Description |
|---|
The PNY NVIDIA RTX 4000 Ada Generation graphics card is built for professionals who require powerful GPU performance in a compact design. It delivers advanced rendering, AI acceleration, and real-time ray tracing capabilities, making it suitable for creative studios, engineers, and data scientists. Often found in small form factor workstations, this card fits conveniently where space is limited but demanding workloads persist. It's commonly used in 3D modeling, video editing, scientific simulations, and other GPU-intensive applications. Key Features
This graphics card is typically deployed in professional environments such as design studios and engineering offices where space constraints are a concern. It enables users to handle demanding graphical or computational projects without sacrificing workstation size. Its balance of power and compactness makes it a reliable choice for those needing high-end GPU resources in smaller setups. |