NVIDIA Tesla M40 : Specifications, Architecture, Working, Differences & Its Applications

The NVIDIA Tesla M40 is a professional GPU accelerator launched on November 10, 2015, as part of the Tesla Maxwell lineup. It succeeded Kepler-based GPUs like the Tesla K10 and became the first Tesla card based on the Maxwell architecture. During its release, it was one of the fastest accelerator GPUs available. At present, the GPU is now commonly found in the used market because of its low cost & large memory capacity. Therefore, it has become a budget-friendly choice for enthusiasts, hobbyists & researchers to run particular ML and AI workloads. This article elaborates on NVIDIA Tesla M40, its working, and applications.

What is NVIDIA Tesla M40?

The NVIDIA Tesla M40 is a high-performance computing GPU that uses the NVIDIA Maxwell architecture. It is designed for professional and data center workloads like AI training, scientific simulations, and deep learning. This GPU significantly reduces deep learning training time.

This GPU features CUDA cores – 3072, TFLOPS – 7 with single-precision performance, and GDDR5 memory available in either 12GB or 24GB. It utilizes a passive cooling system & lacks display outputs, needing it to be installed within a server through its own airflow. This card doesn’t have display outputs, so it cannot be useful in regular desktops, but it is designed completely for computation.

How does NVIDIA Tesla M40 Work?

The NVIDIA Tesla M40 GPU accelerator works with its Maxwell Architecture to speed up complex computational tasks like data analysis and deep learning using many parallel processing cores. Therefore, it performs parallel computations on its various CUDA cores, which are equipped with a huge GDDR5 VRAM – 24 GB to manage large datasets.

Its large amount (12GB/24GB) of VRAM makes it extremely effective for running memory-intensive applications and training large neural networks. In addition, this GPU can also work as a specialized, powerful computational engine in a server environment. Therefore, it performs complex mathematical models for scientific and AI applications much faster than conventional CPUs.

The NVIDIA Tesla M40 core principles are parallel processing, deep learning optimization, and general-purpose graphics processing unit (GPGPU). The M40 GPU uses several specialized processing cores, like CUDA cores, to perform various computational threads concurrently. So this architecture is very efficient for training neural network tasks, which can be broken down into various parallel mathematical operations. This card accelerates deep learning frameworks, which significantly decreases the required time to train sophisticated and large models by processing huge amounts of data in its higher-speed GDDR5 memory.

In addition, this graphics card works as a GPGPU, so its main role is general computation with the CUDA API, but it does not provide graphics for a monitor. In addition, it doesn’t have display outputs, managed through software tools within a server environment.

Specifications

The specifications of NVIDIA Tesla M40 include the following.

• It uses Maxwell 2.0 (GM200) Architecture.
• It features 3072 CUDA Cores.
• Memory is 12 GB/24 GB GDDR5
• Memory Bus is 384-bit
• Memory Bandwidth is 288.4 GB/s
• Clock Speed (Base) is 948 MHz
• Clock Speed (Boost) is 1112 MHz
• Single-Precision Performance is 7.0 TFLOPS
• Double-Precision Performance is 0.21 TFLOPS (FP64)
• Interface is PCI Express 3.0 x16
• Maximum power consumption (TDP) is 250 W
• Power Connector is 8-pin EPS
• Cooling – Passive heat-sink
• Form Factor is Dual-slot, Full-height
• Boost CLK is 1114 MHz
• It doesn’t have display connectors.

NVIDIA Tesla M40 Architecture

The NVIDIA Tesla M40 uses the Maxwell architecture, particularly the Maxwell 2.0 generation. It is designed for power efficiency and high single-precision performance. This architecture enabled the GPU to become one of the best accelerators, especially for deep learning training.

This Maxwell architecture can be optimized for different tasks like deep learning training by providing high throughput and strong single-precision performance using CUDA cores & a large memory. Therefore, it provides a very powerful foundation for deep learning applications, supported by the cuDNN & DIGITS software ecosystem.

Components

The NVIDIA Tesla M40 architecture can be built with different components, like CUDA cores – 3072, a GM200 graphics processor, PCI Express 3.0 x 16 interface for system connectivity & a 384-bit memory interface with GDDR5 memory – 24 GB. In addition, it can also have its passive cooling solution, 288 GB/s memory bandwidth, 250W maximum power consumption, and a passive thermal solution. It uses a PCI Express 3.0 x 16 interface by supporting compute APIs like DirectCompute, CUDA & OpenCL™.

GM200 Graphics Processor

The GM200 in the NVIDIA Tesla M40 is a high-end graphics processor, built on the Maxwell 2.0 architecture with a 28 nm process with 8 billion transistors. It features 3072 shading units, 192 texture mapping units, and 96 ROPs, with a 384-bit memory interface. This graphics processor is used by popular cards like the GeForce GTX 980 Ti & GeForce GTX Titan X.

CUDA Cores

The NVIDIA Tesla M40 GPU has 3072 CUDA cores, which are reliable across both the 24GB/12GB memory variants of the graphics card. These CUDA cores are the basic processing units that are designed for parallel computation. It is essential for tasks like high-performance computing, deep learning model training, and scientific simulations.

These CUDA cores are utilized for a wide range of general-purpose parallel computations, not like specialized Tensor Cores, which are optimized for deep learning matrix operations. They are important in complex calculations and handling large datasets in scientific and professional applications like image analysis, simulations, and speech recognition.

12 GB GDDR5 Memory

The 12 GB GDDR5 Memory is a specific kind of high-speed memory in this GPU, designed to handle large datasets & complex computations. Therefore, it is perfect for demanding professional applications like AI and deep learning. Its 12 GB memory capacity signifies the quantity of video memory for storing data, whereas GDDR5 is the kind of memory technology that provides 288 GB/s of high bandwidth compared to a 384-bit interface.

NVIDIA has connected 12 GB GDDR5 memory with the Tesla M40 using a 384-bit memory interface. This GPU operates at a 948 MHz frequency that can be boosted up to 1112 MHz, where memory runs at 1502 MHz.

384-bit Memory Interface

A 384-bit memory interface is the size of the data path between the GPU’s processor & its GDDR5 memory, which allows it to transmit data at a high rate. This interface can be combined with the type of memory, which provides 288 GB/s of substantial memory bandwidth. It is essential for its intended use within data centers for AI and high-performance computing tasks by ensuring efficient data transmission between the GDDR5 memory and GPU.

288 GB/s of Memory Bandwidth

A 288GB/s memory bandwidth on the NVIDIA Tesla M40 is the rate at which the GPU can transmit data between its video memory and processor cores. This speed is an outcome of the 384-bit memory interface of the card & its GDDR5 memory running at a certain clock speed. It is a critical performance indicator for tasks that need fast access to huge amounts of data, like scientific computations and deep learning training.

7 Tflops of Peak Single-Precision Performance

The NVIDIA Tesla M40 can perform around 7 trillion floating-point operations per second, especially for single-precision calculations. So this metric specifies the highest computational power for GPU tasks like scientific simulations, high-performance computing, and deep learning training applications.

PCI Express 3.0 x16 System Interface

The PCI Express 3.0 x16″ system interface on the GPU is the physical & electrical connection technique used to communicate with the rest of the motherboard & CPU of the computer. It uses the high-bandwidth and 16-lane physical slot of a PCIe 3.0 interface by allowing 16 GB/s of the highest theoretical data transmission speed and enabling seamless integration through compatible server systems.

250 Watts of Maximum Power Consumption

The NVIDIA Tesla M40 has 250 W of the highest power consumption for both the 12 GB & 24 GB memory versions. This power is delivered through an 8-pin EPS power connector & the PCIe slot. So a power supply unit with at least 600 W of optional capacity is recommended for systems with this graphics card. This graphics card uses a passive heat sink for cooling, and it needs enough system airflow within a server chassis to manage this power output.

Compute APIs

The NVIDIA Tesla M40 accelerator supports a wide range of standard and NVIDIA-specific compute APIs. Its CUDA compute capability is 5.2. The supported compute APIs by the NVIDIA Tesla M40 mainly include: NVIDIA CUDA®, OpenCL™, DirectCompute, and OpenACC.

• The NVIDIA CUDA® is a core parallel computing platform & programming model of NVIDIA, which allows developers to use Python, C/C++, and other languages to utilize the thousands of cores of GPUs for general-purpose processing. So the compute capability of Tesla M40 is 5.2.
• This graphics card supports OpenCL™ (Open Computing Language), which is an open standard used for parallel computing across heterogeneous platforms with GPUs.
• DirectCompute is a Microsoft API, used for general-purpose computing on GPUs, which is an element of DirectX 11 & 12.
• The OpenACC is a directive programming standard that simplifies parallel programming of varied GPU/ CPU systems.

Thermal Solution

The NVIDIA Tesla M40 GPU uses a passive thermal solution, designed to be cooled using the high-airflow setting of a server chassis but not by an onboard fan. Thus, it depends on powerful server fans to draw cool air above its heat sink and eject the hot air. Thus, makes it inappropriate for typical desktop PCs without a normal cooling setup.

Software System

The NVIDIA Tesla M40 GPU uses a software system that is a full stack of APIs, drivers, libraries, and tools optimized for the Maxwell architecture of the GPU. So the software system centers on CUDA and & drivers on the NVIDIA Tesla M40, which is optimized for deep learning through cuDNN libraries & DIGITS frameworks. The software system efficiently changes the M40 into a powerful parallel processor from a graphics card for data analytics, AI training, and scientific computing.

How to Maintain NVIDIA Tesla M40?

The steps involved in maintaining the NVIDIA Tesla M40 include ensuring efficient cooling & a dust-free environment, because these are passively cooled graphics cards, designed for server airflow. Thus,  updating software monitoring & driver updates frequently is also necessary for the best performance and lifetime.

• This is a passively-cooled graphics card, which means it does not have built-in fans and depends completely on high airflow in the server chassis.
• Use compressed air to frequently blow debris and dust particles out of the heat sink and the nearby area. Therefore, the buildup of dust can strictly block cooling & lead to overheating.
• Check for any blockages or foreign objects periodically in the PC case that might delay airflow and cause short circuits.
• Software tools must be used to monitor the temperature of the GPU throughout operation. Keep temperatures under 85°C to ensure constant operation & longevity.
• In addition, the specific NVIDIA drivers must be updated because the required drivers may change based on the operating system & intended use.
• Make sure your PSU (power supply unit) & motherboard connections meet the power requirements of the card. This GPU normally uses an 8-pin CPU power connector, which needs compatible PSUs or specific adapters.

NVIDIA Tesla M40 Vs NVIDIA Tesla M60

The difference between NVIDIA Tesla M40 and NVIDIA Tesla M60 includes the following.

NVIDIA Tesla M40	NVIDIA Tesla M60
The Tesla M40 from NVIDIA is a single-GPU card designed for HPC and deep learning.	The Tesla M60 from NVIDIA is a dual-GPU card, designed for graphics virtualization.
Total GPU Memory is 24GB GDDR5	Total GPU Memory is 8GB GDDR5 for each GPU.
Memory Bandwidth is 288 GB/s	Memory Bandwidth is 160 GB/s.
Memory Interface is 384-bit	Memory Interface is 256-bit
CUDA Cores – 3072	CUDA Cores – 2048 for each GPU
Performance is high single-GPU performance, including a large memory pool            .	Performance is higher theoretical raw performance because of two GPUs, except for potential overhead within multi-GPU setups.
Primary uses are: HPC, data science deep learning.	Its primary uses are: virtual and graphics workstations.

Tesla M40 — Who Should Buy it Today?

Although the NVIDIA Tesla M40 is nearly a decade old, it still holds value in 2025 for specific workloads and user groups. It is beneficial for individuals and organizations looking for high compute performance at a low cost without requiring features like ray tracing or Tensor cores. The following users can still benefit from the Tesla M40:

Budget ML and AI Researchers:

Students, entry-level machine learning practitioners, and research labs with tight budgets can use the M40 to train medium-sized neural networks. Frameworks like TensorFlow, PyTorch, and Caffe are well-supported, especially for FP32 computation, where the M40 performs strongly.

Hobby Deep Learning Setups

For home-based GPU clusters or DIY AI labs, the Tesla M40 offers a powerful option to practice CNNs, NLP models, GANs, and reinforcement learning without investing in expensive RTX or A-series accelerators.

Data Centers With Legacy Infrastructure

Enterprises running older servers that rely on PCIe Gen3 can deploy the Tesla M40 as a drop-in accelerator for batch processing, inference workloads, and HPC tasks where memory capacity matters more than new AI features.

Plex/Media Transcoding Servers

Thanks to its hardware acceleration capabilities, the M40 can be used for video transcoding workloads when paired with supported software stacks. It can handle multiple simultaneous streams efficiently in server environments.

Researchers Working on Classical Scientific Simulations

Applications in computational physics, molecular dynamics, and numerical simulations that primarily depend on raw FP32 compute can still take advantage of the M40’s 7 TFLOPS processing capability.

Used GPU Buyers Looking for Value

On the second-hand market, Tesla M40 cards are available at very low cost compared to modern workstation GPUs. This makes them attractive for experimental builds, GPU farms, and cost-sensitive projects.

Relevance of Tesla M40 in 2025

With the rise of newer GPU architectures like Turing (RTX 20), Ampere (RTX 30/A100), Hopper (H100), and Ada Lovelace (RTX 40), the Tesla M40 is no longer considered a top-tier accelerator. However, it still maintains relevance for a segment of users in 2025, provided expectations are realistic.

Where the Tesla M40 Still Performs Well?

Legacy deep learning frameworks and FP32-heavy models. Batch processing and offline training workloads, Scientific computation, HPC, and CUDA GPGPU algorithms, Low-cost server acceleration for educational and lab environments. Workloads where 12 GB VRAM is sufficient, and Tensor cores aren’t mandatory

Where It Falls Behind Modern GPUs?

No Tensor cores → slower AI training vs Turing/Ampere/Hopper No ray tracing support Higher power consumption (250W TDP) compared to newer GPUs PCIe Gen3 limits bandwidth in high-throughput data pipelines FP16/BF16/INT8 performance is outdated for rapid AI model scaling

Advantages

The NVIDIA Tesla M40 advantages include the following.

• The M40 is the fastest accelerator used for training deep neural networks, widely cutting down the required time it takes.
• Its memory capacity is large, like 12GB /24GB, which is essential for training more sophisticated and complex neural networks and managing large datasets.
• It is optimized for ML applications, which integrate well through the software ecosystem of NVIDIA with cuDNN and a variety of deep learning frameworks.
• The M40 can be built & tested in data center environments for high reliability, suitable for continuous operation.
• In addition, it supports NVIDIA GPUDirect by allowing rapid, multi-node neural network training for better performance.
• The M40 GPU is for video transcoding through a faster processing time for different tasks, like proxy creation for videos. Thus, it is helpful for Plex streaming services and home servers.
• It can power high-resource remote workstations for rendering and professional 3D graphics tasks.

Disadvantages

The NVIDIA Tesla M40 disadvantages include the following.

• This GPU card is passively cooled and depends on high-velocity server fans. It needs a separate cooling solution, like a powerful custom fan setup or an AIO liquid cooler, to be used in a standard PC case to stop overheating.
• It needs specific power adapters with a high power draw, which requires a compatible power supply.
• This card lacks video ports; thus, you will require an additional GPU or integrated graphics to obtain a display signal.
• It is much slower compared to modern GPUs for rendering, AI, compute tasks, and many more.
• It has limited driver support.
• This GPU has very poor double-precision performance, making it inappropriate for scientific simulations that need it.

Applications

The NVIDIA Tesla M40 applications include the following.

• This professional-grade GPU accelerator is designed for high-performance computing and deep learning training applications within data centers.
• The Tesla M40 accelerates the training of sophisticated, large deep neural networks.
• This GPU can be used by data scientists with massive datasets to significantly decrease training time.
• It is used within applications that leverage AI, like speech recognition, image & object recognition, facial recognition & natural language processing.
• In addition, the M40 GPU is apt for complex data analysis, general scientific computing & simulations. It needs higher single-precision floating-point performance.
• The M40 handles the huge expansion of data, mostly video content, within large data centers.
• It works with the NVIDIA Hyper Scale Suite software to optimize ML & video processing.

In summary, the NVIDIA Tesla M40 GPU is a powerful and server-grade graphics card. The Tesla M40 is still viable for learning, experimentation, and affordable deep learning setups, especially when sourced from the used market at low pricing. However, for production-scale AI training, large LLM workloads, and mixed-precision models, modern GPUs like the RTX 3090, A100, L40, or H100 offer drastically better efficiency and speed. If the goal is cost-to-performance, the M40 is still attractive in 2025. If the goal is state-of-the-art AI acceleration, newer options are strongly recommended.