What Are GPUs?

GPUs, or graphics processing units, were originally created to render images and video. Their primary function was to offload compute-intensive tasks from the CPU, managing the complex calculations necessary for rendering graphics quickly. Over time, their capabilities have expanded, positioning them as parallel processors capable of handling a variety of demanding computational tasks. This shift has enabled GPUs to play a pivotal role in fields beyond graphics, such as scientific computing, artificial intelligence, and machine learning.

Modern GPUs are highly parallel, allowing them to process many tasks simultaneously, making them ideal for applications requiring significant computational resources over numerous processes. Unlike CPUs, which are optimized for sequential task processing, GPUs can manage thousands of threads at once. This architecture has proven useful for tasks that benefit from parallelism, such as matrix operations key to deep learning algorithms.

This is part of a series of articles about GPU for AI.

Critical GPU Specs for Successful Deep Learning

Tensor Cores

Tensor Cores are specialized hardware components within some modern GPUs that accelerate deep learning workloads. They are optimized for performing mixed-precision matrix operations, such as matrix multiplications and accumulation. Tensor Cores can handle operations in FP16 (16-bit floating point) and FP32 (32-bit floating point) simultaneously.

Clock Speed

Clock speed, measured in MHz or GHz, determines how quickly the GPU cores can execute instructions. A higher clock speed usually means faster processing and more computations per second. For deep learning, while parallelism (number of cores) is more critical, clock speed aids in determining how fast individual computations within those parallel processes are performed.

VRAM

VRAM (Video Random Access Memory) is the dedicated memory available on the GPU to store data needed for computations. It stores the deep learning model parameters, input data, and intermediate computations during training and inference. Larger VRAM allows for bigger batch sizes and more complex models to be processed without memory overflow.

Memory Bandwidth

Memory bandwidth refers to the speed at which data can be read from or written to the GPU’s memory. For deep learning, higher memory bandwidth is crucial since large datasets and model parameters need to be moved quickly between GPU memory and its processing cores. A GPU with higher memory bandwidth allows for faster data transfer, which is critical for training large neural networks where delays in memory access can become a bottleneck.

L2 Cache and L1 Cache

L2 and L1 caches are small, fast memory units closer to the GPU cores, which are used to temporarily store data that is frequently accessed. Caching helps reduce the need to fetch data repeatedly from the slower main memory (VRAM). The size and efficiency of these caches can impact the performance of deep learning models, reducing latency and improving throughput.

CUDA Cores/Stream Processors

CUDA Cores (on NVIDIA GPUs) or Stream Processors (on AMD GPUs) are the primary processing units that execute compute operations on the GPU. The number of CUDA Cores or Stream Processors influences the GPU’s ability to handle parallel computations. For deep learning, a higher count typically means more concurrent operations can be executed.

FP16/FP32/INT8 Performance

The performance of a GPU in various data precision formats determines its efficiency in different stages of deep learning. FP32 (single-precision) is the standard for training due to its accuracy, but FP16 (half-precision) has gained popularity for training and inference to improve speed and reduce memory usage. INT8 (8-bit integer) is often used for inference optimization as it requires even less memory and can accelerate model deployment, albeit with some precision trade-offs.

Compute Capability (FLOPS)

FLOPS (Floating Point Operations Per Second) is a metric that indicates the raw computational power of a GPU. The higher the FLOPS, the more capable the GPU is at performing floating-point calculations. Both single-precision (FP32) and half-precision (FP16) FLOPS are important to consider to balance performance and accuracy.

Multi-GPU Scalability

Multi-GPU scalability refers to the GPU’s ability to work effectively in parallel with other GPUs to accelerate computation. In deep learning, scaling up to multiple GPUs is often necessary to train large models quickly or handle large datasets. Efficient multi-GPU communication, enabled by interconnects like NVLink or PCIe, allows for faster data exchange between GPUs, reducing training times and enabling the distribution of workloads across several GPUs.

Related content: Read our guide to GPU cloud computing

Top GPUs for Deep Learning in 2025

1. NVIDIA A100

Specs:

  • CUDA cores: 6,912
  • Tensor cores: 432
  • GPU memory: 40 GB or 80 GB HBM2
  • Memory bandwidth: 1,555 GB/s
  • Power consumption: 250 to 400 Watts (depending on configuration)
  • Clock speed: 1.41 GHz
  • Thermal Design Power (TDP): 400 Watts

The NVIDIA A100, built on the Ampere architecture, the A100 brings significant performance improvements and excels in tasks involving deep learning model training and inference. It features Tensor Cores, specifically built for AI tasks, which accelerate matrix computations that are essential for deep learning. The A100 supports mixed-precision training, combining FP16 and FP32 formats to balance performance and accuracy.

A key advantage of the A100 is its high memory capacity, with up to 80 GB of HBM2 memory. This large memory pool allows for the handling of extremely large datasets and complex models, which are often required in advanced AI tasks.

Additionally, the A100 incorporates multi-instance GPU (MIG) technology, enabling the division of a single GPU into multiple instances for different tasks. This makes it ideal for data centers and large-scale AI deployments, as multiple workloads can run concurrently on a single A100, maximizing resource efficiency.

2. NVIDIA RTX A6000

Specs:

  • CUDA cores: 10,752
  • Tensor cores: 336
  • GPU memory: 48 GB GDDR6
  • Memory bandwidth: 768 GB/s
  • Power consumption: 300 Watts

The NVIDIA RTX A6000 is a high-performance GPU built for professional applications, including deep learning. Like the A100, it is also based on the Ampere architecture, offering substantial improvements over previous generations in terms of both speed and efficiency. The RTX A6000 comes with 48 GB of GDDR6 memory, which is sufficient for training deep learning models that require a significant amount of memory.

One of the key features of the RTX A6000 is its Tensor Cores, which are optimized for AI tasks and provide accelerated performance for deep learning computations. These Tensor Cores allow for efficient matrix operations, speeding up model training and inference. The RTX A6000 also supports mixed-precision training, which allows for a combination of FP16 and FP32 precisions to enhance both computational speed and memory usage efficiency.

3. NVIDIA RTX 4090

Specs:

  • CUDA cores: 16,384
  • Tensor cores: 512
  • GPU memory: 24 GB GDDR6X
  • Memory bandwidth: 1 TB/s
  • Power consumption: 450 Watts
  • Memory: 24 GB GDDR6X

The NVIDIA GeForce RTX 4090, though primarily marketed as a consumer-grade GPU that targets high-end gamers, however, it can also be used for deep learning tasks. It is based on the Ada Lovelace architecture and features a high number of CUDA cores—16,384—making it an option for researchers and developers working on smaller to medium-sized deep learning models. With 24 GB of GDDR6X memory, the RTX 4090 is capable of handling moderate dataset sizes.

One of the major advantages of the RTX 4090 is its affordability compared to more specialized GPUs like the A100 or A6000. It also benefits from full support for NVIDIA’s CUDA and cuDNN libraries, which are essential for deep learning development. While it lacks enterprise features like multi-instance GPU (MIG) or NVLink support, the RTX 4090’s high memory bandwidth of 1 TB/s allows for fast data transfers, reducing the time required for training and inference.

4. NVIDIA V100

Specs:

  • CUDA cores: 5,120
  • Tensor cores: 640
  • GPU memory: 16 GB
  • Memory bandwidth: 900 GB/s
  • Power consumption: 250 Watts
  • Clock speed: 1.246 GHz

The NVIDIA V100 is a widely-used GPU for deep learning, created specifically for high-performance computing and AI workloads. Built on the Volta architecture, it includes Tensor Cores that are optimized for deep learning tasks, such as matrix multiplications, which are fundamental to training neural networks. The V100 excels in mixed-precision training, allowing for the combination of FP16 and FP32 calculations to optimize both performance and memory usage.

The V100 comes with 32 GB of HBM2 memory, which provides ample capacity for large-scale deep learning models and datasets. Additionally, the V100 supports NVLink, a high-speed interconnect technology that allows multiple GPUs to be linked together, enabling parallel processing across multiple GPUs.

5. NVIDIA A40

Specs:

  • CUDA cores: 10,752
  • Tensor cores: 336
  • GPU memory: 48 GB GDDR6
  • Memory bandwidth: 696 GB/s
  • Power consumption: 300 Watts
  • NVLink support
  • Enhanced for deep learning with Tensor Cores

The NVIDIA A40 is another Ampere-based GPU that offers substantial performance for deep learning tasks. While it is primarily created for professional and data center applications, it can also be utilized effectively for AI workloads. The A40 features 48 GB of GDDR6 memory, providing ample capacity for handling large datasets and complex models that are often required in deep learning.

Similar to other GPUs in the Ampere lineup, the A40 is equipped with Tensor Cores that accelerate AI computations. The A40 also benefits from NVIDIA’s deep learning software ecosystem, including libraries like CUDA, cuDNN, and TensorRT, which optimize the use of GPU resources for AI tasks. While the A40 may not reach the same performance levels as the A100, it is a cost-effective alternative for teams or organizations that need a balance between price and power.

6. NVIDIA L40S GPU

Specs:

  • CUDA cores: 18,176
  • Tensor cores: 568
  • GPU memory: 48GB GDDR6 with ECC
  • Memory bandwidth: 864GB/s
  • Power consumption: Watts
  • Interconnect interface: PCIe Gen4 x16, 64GB/s bidirectional

The NVIDIA NVL40S Tensor Core GPU offers substantial computational power optimized for deep learning tasks, including neural network training and inference. It is based on the Ada Lovelace architecture and includes fourth-generation Tensor Cores and a large number of CUDA cores. L40S supports mixed-precision training and inference, utilizing FP8, FP16, and BF16 precisions.

Its 48 GB of GDDR6 memory allows for the handling of large and complex models common in deep learning workflows. Additionally, the L40S supports NVLink technology, enabling high-speed connectivity between GPUs for multi-GPU training and efficient data transfers.

7. NVIDIA H100 NVL

Specs:

  • CUDA cores: 14592
  • Tensor cores: 456
  • GPU memory: 94GB HBM2e memory
  • Memory bandwidth: 3.9TB/s
  • Power consumption: 700 Watts (350 Watts per GPU)

The NVIDIA H100 NVL GPU is part of the Hopper architecture lineup, suitable for handling large-scale deep learning and high-performance computing (HPC) workloads. The H100 NVL is equipped with dual GPUs, delivering a massive combined 188 GB of HBM3 memory.

H100 NVL can scale across multiple GPUs, supporting advanced interconnect technologies like NVLink and NVSwitch. This multi-GPU scalability makes it suitable for large AI clusters and distributed training, as the GPUs can communicate at high speeds, reducing latency and improving overall throughput.

Key Metrics for Evaluating Your Deep Learning GPU Performance

Even after you choose the right GPU for your project, it’s important to measure how effective your GPU is performing. This will allow you to optimize and get the most out of your hardware.

GPU Utilization

GPU utilization refers to the proportion of GPU resources being used during a task. It is a crucial performance indicator, reflecting how effectively a GPU is employed in processing tasks. High utilization indicates that the GPU is being well-used, which is desirable for maximum efficiency and throughput in deep learning contexts. Conversely, low utilization suggests that the GPU’s capabilities aren’t being fully tapped, possibly due to software inefficiencies or system bottlenecks.

Monitoring GPU utilization is essential in optimizing deep learning performance, as it provides insights into potential areas for improvement in resource allocation. Ensuring high GPU utilization might involve tuning software settings, optimizing model configurations, or balancing workloads to prevent underutilization.

GPU Memory Access and Usage

GPU memory access and usage denote how efficiently a GPU can read and write data to its memory. These metrics significantly affect deep learning performance, where rapid data exchange and efficient memory management enhance modeling processes. Proper memory handling ensures that the GPU has timely access to data, which is crucial for maintaining a high computational throughput during the training and inference of AI models.

Effective GPU memory access and usage are essential for utilizing available resources optimally. Deep learning models often require significant memory bandwidth for data handling, so efficient memory use can prevent bottlenecks, ensuring smoother data flow.

Power Usage and Temperatures

Monitoring power usage and temperatures is vital in maintaining GPU performance and longevity. High power consumption might suggest excessive resource demand, potentially indicating inefficiency, while elevated temperatures can lead to throttling or hardware damage. In deep learning tasks, balancing powerful processing with efficient thermal management ensures a GPU operates safely at optimal performance levels.

Efficient management of power and temperatures involves employing adequate cooling systems and optimizing workload distribution. These factors are important in preventing performance degradation caused by overheating. By understanding power and thermal characteristics, users can employ strategies to maintain GPUs at their peak performance.

Time to Solution

Time to solution is a critical metric for assessing GPU performance, especially in deep learning applications. This metric defines the total time required to complete computational tasks, from training models to deploying them. A shorter time to solution implies high efficiency in terms of cost and productivity, as it reflects how swiftly a GPU can complete the necessary calculations to achieve desired outcomes in AI projects.

Evaluating time to solution involves analyzing performance across many factors, including GPU throughput, memory access, and overall resource management. By focusing on reducing time to solution, researchers and developers can maximize their productivity and streamline processes, enabling rapid iterations and refinements of AI models.

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