NVIDIA Tesla P40 24GB GDDR5 PCI-E 3.0 x16 GPU Accelerator Card

NVIDIA Tesla P40 24GB GDDR5 PCI-E 3.0 x16 GPU Accelerator Card

Product Overview

The NVIDIA Tesla P40 is a data-center-grade GPU accelerator designed for high-performance computing, artificial intelligence inference, machine learning, and virtualization workloads. Unlike consumer graphics cards, the Tesla P40 is optimized for compute efficiency, reliability, and large memory capacity, making it suitable for enterprise servers and professional environments rather than gaming PCs.

It is based on NVIDIA’s Pascal architecture and is widely used in AI inference servers, cloud platforms, and GPU-accelerated data analytics systems.
(Source: NVIDIA Tesla P40 Product Brief – nvidia.com)

Key Specifications

Sources:

• NVIDIA Official Datasheet (nvidia.com)

• TechPowerUp GPU Database (techpowerup.com)

Architecture and Performance

The Tesla P40 uses the Pascal GPU architecture, which delivers strong performance for parallel compute workloads. It is especially effective for AI inference, where INT8 precision allows very high throughput compared to traditional floating-point workloads.

Although it does not include Tensor Cores (introduced in later NVIDIA architectures), the Tesla P40 remains highly capable for inference, video analytics, and batch processing tasks.
(Source: NVIDIA Tesla P40 Datasheet – nvidia.com)

Memory and Bandwidth

One of the main strengths of the Tesla P40 is its 24GB of ECC-protected GDDR5 memory. This large memory capacity allows it to handle:

• Large AI models

• High-resolution image and video datasets

• Virtualized GPU workloads

The 384-bit memory bus and high bandwidth reduce memory bottlenecks during intensive compute operations.
(Source: TechPowerUp – techpowerup.com)

Cooling and Power Requirements

The Tesla P40 uses passive cooling, meaning it relies on high-airflow server chassis rather than onboard fans. This design is standard for data-center GPUs and ensures long-term reliability in rack-mounted environments.

Power consumption can reach 250 watts, so a high-quality power supply and proper thermal management are required.
(Source: NVIDIA Product Brief

Common Use Cases

The NVIDIA Tesla P40 is commonly deployed in:

• AI and deep learning inference servers

• Machine learning workloads

• GPU virtualization (vGPU) environments

• High-performance computing (HPC)

• Video analytics and media processing

It is important to note that the card does not include display outputs such as HDMI or DisplayPort, and it is not intended for gaming or desktop graphics.
(Source: NVIDIA Datasheet – nvidia.com)

Advantages and Limitations

Advantages

• Large 24GB ECC memory for reliability and large datasets

• Strong inference performance for AI workloads

• Enterprise-grade stability and long-term operation

• Suitable for virtualization and multi-user environments

Limitations

• Older Pascal architecture without Tensor Cores

• Requires server-grade cooling and airflow

• Not suitable for gaming or consumer desktop use

Conclusion

The NVIDIA Tesla P40 24GB GDDR5 GPU Accelerator is a powerful and reliable solution for enterprise-level AI inference, virtualization, and compute workloads. Its large memory capacity, ECC support, and high throughput make it well-suited for servers and data-center deployments.

While newer NVIDIA GPUs offer improved efficiency and Tensor Core acceleration, the Tesla P40 remains a strong option for organizations seeking cost-effective inference performance with proven stability.
(Source: NVIDIA Tesla P40 Product Brief – nvidia.com)

Frequently Asked Questions (FAQ)

Is the Tesla P40 suitable for gaming?
No. It has no display outputs and is designed strictly for compute workloads.

Can it be used in a desktop PC?
Technically yes, but it requires strong airflow and is not practical for standard desktop use.

Does it support AI frameworks like TensorFlow and PyTorch?
Yes. It supports CUDA-based frameworks commonly used in AI and machine learning.

What is the main advantage of the Tesla P40?
Its 24GB ECC memory and high inference performance make it ideal for data-center AI workloads.