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5 best and most cost-effective deep learning GPU systems

Emmanuel Ohiri

Emmanuel Ohiri

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?

As previously discussed, 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

Choosing the right GPU for deep learning involves considering several factors; the size of the GPU memory is critical as it determines the size of the neural network you can train. A larger memory will allow you to train larger models or use larger mini-batch sizes.

The computational power of the GPU, typically measured in floating-point operations per second (FLOPS), is another crucial consideration. A higher FLOPS value indicates a more powerful GPU.

Energy efficiency is also an essential factor, especially for those planning to run large-scale training jobs. More energy-efficient GPUs can lead to significant savings in electricity costs.

Finally, the price of the GPU should align with your budget without compromising the performance requirements of your deep learning tasks.

Other factors to take into consideration:

  1. CUDA Cores: The number of CUDA cores directly influences the GPU's processing power. More CUDA cores usually translate to faster training times.
  2. Memory (VRAM): Deep learning models with large datasets require ample memory for efficient training. Ensure that your GPU has sufficient VRAM to accommodate your model and data.
  3. Memory Bandwidth: A high memory bandwidth facilitates the quick exchange of data between the GPU and the memory, which is crucial for speedy calculations.
  4. Tensor Cores: Tensor cores, found in some GPUs, are specialised units for matrix operations commonly used in deep learning. They can significantly accelerate certain operations.
  5. Compatibility: Ensure the GPU is compatible with your existing hardware and software stack. Also, check if it supports popular deep learning frameworks like TensorFlow and PyTorch.

Related: What are the recommended GPUs for running machine learning algorithms?

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 A100The 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 V100Few 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 VIIPay 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 8000The 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 K80The 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.

Leveraging The Cloud and CUDO Compute for Deep Learning

Cloud computing is an excellent option for businesses and individuals who require occasional deep learning processing but want to avoid investing in a powerful GPU. CUDO Compute offers a unique approach to GPU computing by providing a cloud-based platform that harnesses the idle power of networked servers worldwide. The platform delivers on-demand access to high-performance GPUs, allowing you to scale your deep learning projects according to your needs.

The pay-as-you-go model ensures you only pay for the resources you use, making it a cost-effective option for various budgets. This solution is cost-effective and environmentally friendly, reducing the need for new hardware and lowering energy consumption.

For businesses looking to optimise deep learning processes, CUDO Compute offers a practical, cost-effective solution. It provides scalable GPU resources that can handle complex deep-learning tasks, making it an excellent choice for businesses of all sizes.

GPUs have revolutionised the field of deep learning by offering unmatched processing power and parallelism. Their ability to handle complex calculations efficiently has made them an indispensable tool for researchers, developers, and enthusiasts. The GPUs mentioned in this article offer excellent options for various budgets and requirements. Additionally, CUDO Compute provides flexibility and scalability, enabling you to harness the power of GPUs without a significant upfront investment. Your deep learning journey can be productive and cost-efficient with the right GPU and tools.

About CUDO Compute

CUDO Compute is a fairer cloud computing platform for everyone. It provides access to distributed resources by leveraging underutilised computing globally on idle data centre hardware. It allows users to deploy virtual machines on the world’s first democratised cloud platform, finding the optimal resources in the ideal location at the best price.

CUDO Compute aims to democratise the public cloud by delivering a more sustainable economic, environmental, and societal model for computing by empowering businesses and individuals to monetise unused resources.

Our platform allows organisations and developers to deploy, run and scale based on demands without the constraints of centralised cloud environments. As a result, we realise significant availability, proximity and cost benefits for customers by simplifying their access to a broader pool of high-powered computing and distributed resources at the edge.

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