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Product Overview |
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The NVIDIA Tesla P4 8GB GDDR5 Video Graphics Card delivers specialized acceleration for AI inference and video processing tasks. It fits well in data center environments where space and power efficiency are critical, serving as a practical solution for enhancing machine learning workloads without requiring large-scale hardware changes. |
General Information |
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| Brand | Nvidia |
| Part Number | 900-2G414-0300-000 |
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, CUDA, CUDA: 7.0, Shader Model: 5.1, DirectCompute |
| Power Connectors | None (powered by PCIe slot) |
Memory |
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| Memory Size | 8GB |
| Interface | GDDR5 |
| Memory Bus | 256-bit |
Physical Characteristics |
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| Slot Width | Single-slot, Low Profile |
| 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 is built to accelerate AI inference workloads in data centers, delivering high efficiency in a compact form factor. It's often found in servers handling machine learning inference, video analytics, and real-time data processing. Designed primarily for IT professionals, researchers, and developers working with AI applications, the Tesla P4 offers a blend of performance and power efficiency. Its architecture allows for seamless integration into existing infrastructures requiring dense GPU deployments. Key Features
In practice, this card is commonly deployed in data centers focused on AI-driven services or real-time analytics. Its low power draw and compact size make it suitable for dense server racks, helping organizations scale AI workloads without excessive energy costs. |
Use Cases |
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The NVIDIA Tesla P4 GPU is primarily deployed in data center environments focused on AI inference and video analytics. It suits organizations requiring efficient acceleration of machine learning models in dense server racks where power and space constraints are critical factors. How It's Used:
This GPU facilitates consistent performance gains while maintaining low power usage and compact form factors, which is essential for scaling AI applications efficiently in constrained data center footprints. |