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Product Overview |
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The NVIDIA Tesla P4 Video Graphics Card offers a balance between compact design and performance, making it suitable for tasks involving video processing and AI workloads. It fits into systems where space is limited but efficient and steady graphics acceleration is necessary. |
General Information |
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| Brand | Nvidia |
| Part Number | 900-2G414-0000-000 |
Technical Information |
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| Chipset | Nvidia |
| Bus Interface | PCI Express 3.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.5, OpenCL, Vulkan: 1.2.175 , CUDA, CUDA: 7.0, Shader Model: 5.1, DirectCompute |
| Power Connectors | None (powered via PCIe slot; typical card TDP ~50-75W) |
Memory |
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| Memory Size | 8GB |
| Interface | GDDR5 |
| Memory Bus | 256-bit |
Physical Characteristics |
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| Slot Width | Low-profile, Single-slot |
| Weight | 3.00 |
| Condition | Refurbished |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
Product Description |
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The NVIDIA Tesla P4 Video Graphics Card is designed to accelerate video processing and graphics tasks. It is often found in data centers and edge servers where efficient AI inference and video analytics are critical. Built for professionals working in machine learning, video streaming, and real-time image processing, this card offers reliable performance in a compact form factor. Key Features
This graphics card is typically deployed in edge computing setups and AI inference servers where space and power efficiency matter. Its capabilities help streamline video analytics and accelerate workloads that require high-throughput and low latency. The Tesla P4 fits well in environments needing reliable, continuous processing without bulkier hardware. |
Use Cases |
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The NVIDIA Tesla P4 graphics card is predominantly utilized in environments where space constraints demand compact hardware without sacrificing processing power. It serves enterprise IT and data center operations requiring efficient acceleration of video analytics, machine learning inference, and multimedia workloads within rack-mounted or edge computing systems. How It's Used:
This GPU enhances operational efficiency by delivering steady graphics acceleration in constrained form factors, enabling scalable deployment across diverse infrastructure. Its design supports consistent performance in environments prioritizing density and resource optimization. |