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| Product Overview |
|---|
The HP NVIDIA Tesla P4 8GB card delivers dedicated GPU acceleration tailored for AI-driven computing tasks. It fits well in data center setups focused on inference workloads, providing a solid mix of speed and efficiency without occupying excessive space or power. |
| General Information | |
|---|---|
| Brand | HP |
| Part Number | Q0V79A |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCI Express 3.0 x16 |
| Supported APIs | OpenCL, CUDA, DirectCompute |
| Output Interface | None (compute card, no display outputs) |
| Power Connectors | None (powered by PCIe slot) |
| Memory | |
|---|---|
| Memory Size | 8GB |
| Interface | GDDR5 |
| Memory Bus | 256-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Single-slot, Low-profile |
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
| Product Description |
|---|
The HP NVIDIA Tesla P4 is a specialized graphics card designed to accelerate AI inference and machine learning tasks. It is built for data centers and enterprise environments where efficient, high-throughput processing is essential. This card is often found in servers and workstations that require enhanced GPU performance without compromising power efficiency. Key Features
Typically deployed in AI inference servers and machine learning clusters, the Tesla P4 handles real-time analytics and image recognition effectively. Its design allows organizations to run complex models at scale without requiring extensive hardware resources. |
| Use Cases |
|---|
The HP NVIDIA Tesla P4 8GB graphics card is primarily deployed in data center environments where AI inference and deep learning workloads demand high throughput and energy efficiency. Ideal for enterprises managing real-time analytics and virtualized GPU environments, this card supports users requiring compact, scalable solutions for accelerated computing. How It's Used:
This GPU enables operational efficiency by delivering targeted acceleration tailored to inference workloads, facilitating scalable AI deployments without excessive power or space demands. Its integration into modern server architectures helps maintain high performance while supporting evolving computational needs. |