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| Product Overview |
|---|
The NVIDIA Tesla P40 24GB graphics card is a powerful computing device built to handle intensive AI and high-performance computing tasks. It fits into server and workstation setups where large-scale data processing and deep learning model training demand significant GPU performance and memory capacity. |
| General Information | |
|---|---|
| Brand | Nvidia |
| Part Number | 872323-001 |
| Series | Tesla P40 |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| GPU Clock | 1303 MHz |
| Bus Interface | PCIe 3.0 x16 |
| Output Interface | No Outputs |
| Suggested PSU | 600 W |
| Power Connectors | 8-pin EPS |
| Memory | |
|---|---|
| Memory Size | 24GB |
| Interface | GDDR5 |
| Memory Bus | 384 bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Dual-slot |
| Weight | 4.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Product Description |
|---|
The NVIDIA Tesla P40 24GB graphics card is tailored for demanding AI inference, deep learning, and high-performance computing tasks. Often found in data centers and professional workstations, it accelerates complex computations and machine learning models efficiently. Built for scientists, researchers, and engineers working on AI projects or large-scale simulations, this card provides the processing power needed to handle massive datasets and real-time analytics. Key Features
This GPU is commonly deployed in AI research labs, enterprise data centers, and deep learning training rigs. It enables smoother, quicker experimentation and scaling of neural networks without frequent memory bottlenecks. The Tesla P40 plays a crucial role in accelerating workloads where time and accuracy are critical, making it a staple in environments pushing the boundaries of machine learning and scientific computing. |
| Use Cases |
|---|
The NVIDIA Tesla P40 24GB GPU is predominantly utilized in enterprise and research settings requiring extensive parallel processing and large memory capacity. It serves organizations focusing on AI model training, scientific simulations, and data analytics that demand robust computational throughput. How It's Used:
This GPU supports operational scalability by providing substantial compute resources that improve throughput and reduce processing times. Its integration into diverse infrastructures enables organizations to optimize workload execution and efficiently manage resource-intensive applications. |