Discover the Best GPUs for Your Next Deep Learning Project
GPUs were originally designed to enhance graphical tasks; however, in the current era they can accelerate computational processes for deep learning at new levels. They play a crucial role in modern artificial intelligence systems, with new GPUs being specifically optimized for deep learning tasks.
In this blog post, we’ll try to find out the answer to the questions: why GPUs are beneficial for deep learning projects, what are the differences between consumer-grade GPUs and data center GPUs. So, without further ado, let’s dive into the details.
WHAT ARE GRAPHIC PROCESSING UNITS?
Graphics processing units (GPUs) are specialized units used to speed up computational tasks. Initially developed for processing images and visual data, GPUs are now being applied to enhance a wide range of computational tasks, including deep learning. This is because GPUs can efficiently handle multiple simultaneous computational tasks, making them well-suited for distributed computing.
While many central processing units (CPUs) have multiple cores operating with:
- Multiple Data (MIMD) architecture
- Multiple Instruction
GPUs utilize a Single Instruction, Multiple Data (SIMD) architecture. This difference makes GPUs particularly suitable for deep learning tasks that involve performing the same operation on many pieces of data simultaneously.
FACTORS DICTATE YOUR GPU BUYING DECISION
When designing your deep learning architecture, your decision to include GPUs relies on several factors:
Optimization Needs
One drawback of GPUs is that optimizing lengthy individual tasks can occasionally be trickier compared to CPUs.
Dataset You Need to Work On
GPUs working together in parallel can expand more smoothly than CPUs, allowing you to handle huge datasets more swiftly. The bigger your datasets become, the more advantage you can derive from utilizing GPUs.
Memory Bandwidth Requirement
Incorporating GPUs can offer the necessary capacity to handle extensive datasets. This is due to GPUs having their own dedicated video RAM (VRAM), allowing you to free up CPU memory for additional tasks.
HOW DO MODERN DEEP LEARNING FRAMEWORKS USE GPUS?
Following the introduction of CUDA by NVIDIA, a range of deep learning frameworks such as PyTorch and TensorFlow emerged. These frameworks streamline the process of programming directly with CUDA, making GPU processing accessible for modern deep learning configurations.
EXAMPLES OF GPUS TAILORED FOR DEEP LEARNING PROJECTS
When adding GPUs to your deep learning setups, you have various options, with NVIDIA being the dominant player in the market. These options include GPUs meant for regular consumers, those for data centers, and managed workstations.
Data Center GPUs
NVIDIA Tesla K80
This gadget offers a maximum of 24 gigabytes of memory and a performance of 8.73 teraflops. It's tailored for data analysis and scientific calculations, utilizing the Kepler architecture.
NVIDIA v100
This model offers a maximum of 32GB of memory and achieves 149 teraflops of performance. It utilizes NVIDIA Volta technology and was specifically crafted for high-performance computing (HPC), as well as machine learning and deep learning tasks.
NVIDIA Tesla P100
This device offers 16 gigabytes of memory and a performance of 21 teraflops. It's intended for high-performance computing and machine learning tasks, utilizing the Pascal architecture.
NVIDIA A100
This unit provides 40 gigabytes of memory and achieves a performance level of 624 teraflops. It's designed for high-performance computing (HPC), data analysis, and machine learning duties. Additionally, it integrates multi-instance GPU (MIG) technology to enable broad scaling.
CONSUMER-GRADE GPUS
NVIDIA GeForce RTX 2080 Ti
This device offers 11 gigabytes of memory and delivers 120 teraflops of performance. It's crafted with gaming enthusiasts in mind rather than for professional purposes, and it's also constructed using NVIDIA's Turing GPU architecture.
NVIDIA Titan RTX
This unit offers 24GB of memory and achieves a performance level of 130 teraflops. It features both Tensor and RT Core technologies and is built upon NVIDIA's Turing GPU architecture.
NVIDIA Titan V
This GPU provides memory options between 12GB and 32GB, and its performance varies from 110 to 125 teraflops, depending on the version. It's equipped with Tensor Cores and utilizes NVIDIA's Volta technology.
FAQS
What is a good graphics processing unit (GPU) for deep learning?
NVIDIA's GPUs like A100, A6000, and V100 are popular picks for deep learning because they have lots of tensor cores and big memory capacities. But they can be pricey, often running into thousands of dollars.
Is it necessary to acquire GPU programming skills to dive into deep learning applications?
No, it is like starting from the ground up. Since deep learning tasks demand significant processing power, most frameworks are designed to work with distributed servers, multi-CPU setups, and even GPUs.
Getting familiar with configuring the frameworks to utilize the GPU and installing the necessary GPU libraries like CUDA or OpenCL is enough. Additionally, you may need to learn how to adjust your program's structure to handle parallel processing according to your specific needs.
Can I buy a GPU for deep learning for under $100?
You can go for refurbished options rather than going for a new one. Radeon HD 6950 is a one of the good options for your needs.
How much GPU memory is enough for deep learning?
The quantity of GPU memory necessary for deep learning may fluctuate depending on the dataset's size and the complexity of the neural network's structure. While a GPU with 4-8 GB of memory could be sufficient for modest datasets and simple network architectures, larger datasets and more complex models may require a GPU with at least 16 GB of memory, or ideally even more.
WRAPPING UP
In short, best GPUs for deep learning is a broad topic. At this point, you are familiar with the basic definitions and terminologies and good options in the market, So, it is time to make informed buying decisions and take your deep learning project to the next level.
