Cost-Effective Deep Learning GPU Systems: Top 5 Picks (2024)

Deep learning is transforming industries and creating new possibilities in healthcare, finance, entertainment, and many more. However, deep learning algorithms require powerful hardware to run efficiently and effectively. Access to hardware is essential for the smooth training of complex neural networks.

Graphical Processing Units ( GPUs) have emerged as the backbone of deep learning systems, surpassing the capabilities of Central Processing Units ( CPUs) due to their parallel processing capabilities. However, this comes at a premium because GPUs are pricey. This article will help you understand how GPUs work and identify the top five cost-effective ones for deep learning.

What is Deep Learning?

Deep learning is a subset of machine learning that employs algorithms to model high-level abstractions in data. This method is based on the structure and function of the brain, often referred to as artificial neural networks. Deep learning simulates how our brain works, enabling systems to learn, identify objects, and perform complex tasks with increasing accuracy without human intervention. This machine-learning technique teaches computers to do what comes naturally to humans: learn by example.

Deep learning can automatically learn and improve from experience, examining algorithms and adjusting them for better performance. It’s a powerful tool in artificial intelligence (AI), imitating how humans gain specific knowledge.

What is GPU for Deep Learning?

GPUs are electronic circuits specialised in manipulating and altering memory at high speeds to speed up image creation in a frame buffer intended to be displayed on a device. GPUs have been repurposed in deep learning due to their ability to perform multiple, simultaneous computations.

The parallel architecture of GPUs makes them exceptionally well-suited for intensive computation tasks like deep learning. They excel at executing matrix operations and performing multiple calculations simultaneously, which are fundamental in training neural networks-making GPUs indispensable tools for deep learning.

How do GPUs for Deep Learning Work?

Single Instruction Multiple Data (SIMD) Stream Architecture is widely used in GPUs today. This design enables them to perform parallel operations and perform more efficiently for specific tasks. When training a deep learning model, the model’s parameters are updated using gradient descent optimization algorithms, which involve numerous matrix calculations.

The parallel processing capability of GPUs is beneficial for deep learning tasks, as they require extensive computations on large amounts of data. GPUs excel at performing these matrix operations in parallel, resulting in a substantial speedup in training times.

GPU-accelerated deep learning frameworks offer flexibility to design and train custom deep neural networks. These frameworks provide interfaces to commonly used programming languages, making it easier for developers to leverage the power of GPUs.

How to Choose the Best GPU for Deep Learning

When selecting a graphics processing unit (GPU) for deep learning tasks, there are various important aspects to consider. One key aspect is the size of the GPU’s memory because it dictates how big the artificial neural networks that you can train can be. Therefore, having more GPU memory lets you work with bigger models or increase the batch size during training.

Another major factor to consider is the GPU’s computing power, often expressed in terms of floating-point operations per second (FLops). In general, a greater FLop count signifies a stronger GPU.

Moreover, energy efficiency becomes relevant when conducting extensive training projects. Energy-efficient GPUs can help reduce electrical expenses over time.

Lastly, cost plays a vital role. You must find a balance between affordability and meeting the performance needs of your specific deep-learning activities.

Additionally, here are other elements worth taking into account:

• CUDA cores: Their quantity impacts the GPU’s ability to process information. Normally, a higher number of CUDA cores translates to shorter training periods.
• Memory (VRAM): For effective training, ensure your GPU has enough video RAM to manage your dataset and deep learning model.
• Memory bandwidth: This feature enables swift data transfers between the GPU and its memory, which is beneficial for executing complex calculations promptly.
• Tensor cores: Some GPUs have specialized ones designed explicitly for handling matrix operations that are common in deep learning. These components can considerably boost particular processes.
• Compatibility: Verify whether the GPU works seamlessly with your equipment and software configuration. Make sure it supports widely-used deep learning frameworks, such as TensorFlow and PyTorch.

Top 5 Cost-Effective GPUs for Deep Learning

Several GPUs on the market deliver commendable performance for deep learning tasks. Some notable ones include NVIDIA’s GeForce GTX 1070 and GeForce GTX 1080 Ti, which balance cost and performance well.

Five exceptional GPUs that deliver remarkable performance at a reasonable price point:

• NVIDIA Tesla A100: The NVIDIA Tesla A100 is a game-changer in the realm of deep learning. The innovative Ampere architecture powers it, offering 312 teraflops of FP16 performance. With its Multi-Instance GPU (MIG) technology, you can partition the A100 into up to seven separate GPUs, each capable of running independent tasks. This versatility ensures efficient utilisation, making the A100 cost-effective for deep learning needs.
• NVIDIA Tesla V100: Few GPUs can match the deep learning capabilities of the NVIDIA Tesla V100. With 640 Tensor Cores and a peak performance of 125 teraflops, this GPU is designed to handle the most demanding deep-learning tasks. Its high-bandwidth memory and large cache size ensure smooth data transfer, speeding up your model’s training times. The V100 is a powerhouse that offers both performance and value.
• AMD Radeon VII: Pay attention to the AMD Radeon VII when considering GPUs for deep learning. It efficiently handles complex computations with a peak performance of 13.8 teraflops and 16GB of high-bandwidth memory. Its advanced 7nm process technology boosts performance while reducing power consumption. The Radeon VII is a powerful and affordable choice.
• NVIDIA Quadro RTX 8000: The NVIDIA Quadro RTX 8000 stands out for its massive 48GB of memory, allowing you to train larger, more complex models. Built on the Turing architecture, it features RT cores for real-time ray tracing and Tensor cores for AI acceleration. This GPU promises top-tier performance without breaking the bank.
• NVIDIA K80: The NVIDIA K80 is a cost-effective solution for those starting with deep learning. Although older than the other GPUs on this list, it still offers respectable performance, with 4992 CUDA cores and a peak performance of 8.73 teraflops. The K80 provides an affordable entry point into the world of deep learning.

Originally published at https://blog.spheron.network on May 21, 2024.