699-2G600-0202-910 Nvidia GPU Tesla M40 Computing Accelerator

Detailed of Nvidia Tesla M40 GPU GDDR5 

General Product Description

• Brand: Nvidia Corporation
• Part Number: 699-2G600-0202-910
• Device Classification: High-Performance Graphics Card
• Product Type: GPU Computing Accelerator

Core Architecture and Processing Power

Graphics Engine and Chipset Details

• GPU Framework: NVIDIA Volta Architecture
• Chipset Manufacturer: Nvidia
• Chipset Family: Tesla Series M
• Chipset Model: Tesla M40

Supported Graphics APIs

• DirectX 12
• OpenGL 4.5
• OpenCL
• DirectCompute 5.0

Memory Configuration

Video Memory Specifications

• Installed Memory: 12GB GDDR5
• Memory Interface Width: 384-bit
• Memory Type: GDDR5 Technology

Physical Design and Cooling

Form Factor and Dimensions

• Card Format: Plug-in Module
• Slot Requirement: Dual-Slot
• Card Height: Full-Height
• Cooler Type: Passive Cooling System
• Height: 4.4 inches
• Length: 10.5 inches

Why Choose Nvidia Tesla M40 GPU?

• Optimized for scientific computing, AI training, and data analytics
• Robust memory bandwidth with 384-bit interface for high-speed data processing
• Supports advanced APIs for versatile development environments
• Passive cooling ideal for quiet and efficient server deployments
• Reliable performance with Tesla architecture for enterprise-grade workloads

Nvidia Tesla M40 Computing Accelerator Overview

The Nvidia 699-2G600-0202-910 GPU, widely recognized as the Tesla M40 Computing Accelerator, represents a pinnacle in high-performance GPU technology designed specifically for data-intensive computing environments. With its robust architecture and powerful parallel processing capabilities, this accelerator is optimized for deep learning, scientific simulations, artificial intelligence workloads, and large-scale enterprise computing. By integrating advanced memory technologies and superior computational power, the Tesla M40 enables organizations to achieve unprecedented efficiency and performance in GPU-accelerated tasks.

High-Performance Architecture of the Tesla M40

The Tesla M40 is built on Nvidia’s Maxwell architecture, which offers a balance between high throughput and energy efficiency. It features 3072 CUDA cores that deliver exceptional parallel processing capability, allowing for the rapid execution of complex computations. The GPU's high clock speeds are engineered to maintain consistent performance across sustained workloads, making it ideal for machine learning training and inferencing. Additionally, the Tesla M40 supports ECC memory, which ensures computational reliability by detecting and correcting memory errors, a crucial requirement for mission-critical applications in scientific computing and enterprise AI workloads.

Memory and Bandwidth Capabilities

The Tesla M40 is equipped with 12GB of GDDR5 memory, providing ample capacity for handling large datasets. This substantial memory, combined with a 288 GB/s memory bandwidth, ensures that data can be accessed and processed at high speeds without bottlenecks. The GDDR5 memory technology enhances the card’s ability to perform high-speed data transfers between the GPU and system memory, making it well-suited for applications that demand massive parallel data processing such as neural network training and high-resolution simulation tasks.

GPU Computing for Deep Learning

One of the primary applications of the Nvidia Tesla M40 is in the field of deep learning. Leveraging the massive parallelism offered by its CUDA cores, the Tesla M40 can accelerate neural network training by significantly reducing computation time compared to traditional CPUs. It is compatible with popular deep learning frameworks such as TensorFlow, PyTorch, and Caffe, allowing researchers and developers to integrate the GPU seamlessly into existing machine learning pipelines. Its computational power ensures that models with millions of parameters can be trained efficiently, enabling faster experimentation and innovation in AI research.

Optimized for AI Inference

In addition to training capabilities, the Tesla M40 is also highly effective for AI inference tasks. Once a neural network has been trained, deploying it to make predictions in real-time requires consistent and reliable GPU performance. The Tesla M40 delivers this through its high-throughput architecture and optimized memory pathways. Enterprises that deploy AI models for applications such as image recognition, natural language processing, and predictive analytics benefit from reduced latency and increased throughput, which in turn translates into faster decision-making and improved operational efficiency.

Enterprise and Data Center Integration

The Tesla M40 is engineered to meet the rigorous demands of enterprise and data center environments. Its passive cooling design allows it to be integrated into server systems with shared airflow, which is essential for high-density deployments. Additionally, it supports Nvidia GPU Direct technology, enabling direct memory access between GPUs and other system components, thereby reducing data transfer overheads and latency. This makes the Tesla M40 an excellent choice for high-performance computing clusters where multiple GPUs operate in tandem to handle massive computational loads efficiently.

Energy Efficiency and Thermal Management

While delivering exceptional performance, the Tesla M40 is also designed with energy efficiency in mind. The Maxwell architecture provides a balance between high computational throughput and power consumption, ensuring that performance does not come at the cost of excessive energy use. Thermal management is critical in data centers, and the M40’s design allows it to maintain optimal operating temperatures even under sustained high workloads. This ensures reliable performance while minimizing the risk of overheating and potential downtime.

Software and Driver Ecosystem

The Nvidia Tesla M40 benefits from a comprehensive software ecosystem that maximizes its computational potential. Nvidia provides a suite of developer tools, libraries, and APIs such as CUDA, cuDNN, and NCCL, which enable developers to optimize applications for maximum GPU performance. These tools allow precise control over memory allocation, parallel execution, and inter-GPU communication, enhancing both development efficiency and application performance. Regular driver updates and software optimizations ensure that the Tesla M40 continues to deliver cutting-edge performance for a wide range of computational tasks.

Compatibility and Scalability

The Tesla M40 is compatible with PCIe 3.0 interfaces, ensuring broad compatibility with modern server platforms. Its passive form factor allows for deployment in multi-GPU configurations, enabling organizations to scale computational resources according to workload requirements. High scalability is particularly important for large-scale AI projects, scientific simulations, and big data analytics, where multiple GPUs need to operate in concert to process massive datasets efficiently. The Tesla M40’s ability to scale without compromising performance makes it a preferred solution for enterprises seeking to future-proof their GPU infrastructure.

Use Cases in Machine Learning and AI Research

Beyond scientific computing, the Tesla M40 finds extensive applications in machine learning and AI research. Universities, research labs, and AI startups utilize its processing power to develop advanced models for computer vision, natural language understanding, reinforcement learning, and generative AI. Its high-throughput architecture allows researchers to experiment with deeper and more complex neural networks, resulting in more accurate predictions and advanced AI capabilities. Furthermore, the Tesla M40’s ecosystem supports distributed training across multiple GPUs, significantly reducing the time required to train state-of-the-art AI models.

Future-Proofing High-Performance Computing

The Tesla M40 also supports forward-looking computing paradigms by offering features that align with the evolving demands of AI and high-performance computing. Its compatibility with current software frameworks and scalability for multi-GPU setups ensures that organizations can expand computational resources as needed without significant infrastructure changes. By integrating Tesla M40 GPUs into their computing clusters, enterprises can future-proof their systems, ensuring readiness for emerging workloads and increasingly complex computational challenges.