NVIDIA Deep Learning GPUs provide high processing power for training deep learning models. This article provides a review of three top NVIDIA GPUs—NVIDIA Tesla V100, GeForce RTX 2080 Ti, and NVIDIA Titan RTX.
An NVIDIA Deep Learning GPU is typically used in combination with the NVIDIA Deep Learning SDK, called NVIDIA CUDA-X AI. This SDK is built for computer vision tasks, recommendation systems, and conversational AI. You can use NVIDIA CUDA-X AI to accelerate your existing frameworks and build new model architectures.
In this article, you will learn:
This is part of an extensive series of guides about NVIDIA A100
NVIDIA CUDA-X AI is a software development kit (SDK) designed for developers and researchers building deep learning models. It leverages high-performance GPUs and meets a range of industry benchmarks, including MLPerf.
NVIDIA CUDA-X AI is designed for computer vision tasks, recommendation systems, and conversational AI. You can use it to accelerate your existing frameworks and build new model architectures. CUDA-X AI libraries provide a unified programming model that enables you to develop deep learning models on your desktop. You can then deploy those models directly to both datacenters and resource limited devices, such as Internet of Things (IoT) devices
Image source: NVIDIA
The NVIDIA Deep Learning SDK includes libraries for the following functionalities:
When setting up your deep learning infrastructure there are multiple GPU options you can choose from. Below are NVIDIA options, commonly considered as the most powerful GPUs on the market for deep learning.
NVIDIA Tesla V100
The NVIDIA Tesla V100 is optimized and includes extra features well suited to machine learning and deep learning implementations. These features include high-memory bandwidth, AI acceleration, specialized tensor cores, and a large amount of VRAM.
The Tesla V100 comes in both 16 and 32GB memory versions. It can also provide up to 125 teraflops of performance when used in combination with an NVIDIA Volta architecture. The downside is that it is one of the most expensive NVIDIA options available. Despite this disadvantage, Tesla V100 is considered a highly useful and popular NVIDIA Deep Learning GPU.
GeForce RTX 2080 Ti
The GeForce RTX 2080 Ti is a GPU designed for budget operations and small-scale modeling workloads. It provides 11GB of memory and can be used in configurations of up to four GPUs per workstation. This is due to a unique blower design that enables more dense configurations than with other units.
Although less powerful than the Tesla V100, the NVIDIA RTX 2080 Ti can provide up to 80% of the speed when training neural networks. It does this at an almost seven times lower cost than the Tesla.
NVIDIA Titan RTX
The NVIDIA Titan RTX is a mid-range GPU option. It offers 24GB of VRAM and you can pair it with the NVLink bridge to increase this to 48GB. The Titan RTX enables you to perform full rate mixed-precision training and operates 15- 20% faster than Tensor Cores.
The downside of the Titan RTX is its twin fan design, which prevents stacking units in a workstation. While this GPU can be combined with others, it would require significant modifications to the cooling mechanism. So, unless you must implement mixed-precision training, you might want to consider one of the other NVIDIA Deep Learning GPUs.
You can find more options in our article, which can help you find the best GPU for deep learning.
When using NVIDIA GPUs and the deep learning SDK there are a few best practices that can help you ensure the best performance. Some of these practices are introduced below.
Enable Tensor Cores
Tensor Cores are processing cores that are designed specifically to speed deep learning processes. You can effectively use Tensor Cores with INT8, FP 16, or FP 32 data. For the latter, you can use mixed precision methods. Meanwhile, you should choose a number of key parameters divisible by eight with FP16 and 16 if using INT8.
Whether you can use these cores is determined by your parameters. These determinations vary by architecture type.
Operate in math-bound situations where possible
GPUs are designed to increase the performance of calculations in parallel. However, this requires the loading and storage of data, meaning that performance may be limited by bandwidth or memory.
This often happens when operations cannot be represented by matrix multiples, such as with pooling, batch normalization, or activation functions. In these situations, you can either increase your bandwidth or your memory capacity. Alternatively, you can prioritize implementations that are math-bound. This means that performance is limited by the number of calculations GPUs can perform (which can be upped by enabling Tensor Cores).
Choose parameters to maximize execution efficiency
Due to the parallel processing format of GPUs, you need to be mindful of how evenly your parameters can be broken up. The more even the division, the better the performance gains you’ll see. In general, you should aim for parameters to be divisible by an even number somewhere between 64 and 256.
You can use values larger than 256 but only with decreasing gains. Additionally, finding the right combination of limited parameters and high divisibility will give you the best performance. This assumes that your operations are math-bound with a high arithmetic intensity (i.e. more floating point operations than memory accesses).
GPUs are a critical component of machine learning infrastructure pipelines. Your GPUs determine the processing power of your models, and influence your overall performance and budget. If GPU resource allocation is not properly configured and optimized, you can quickly hit compute or memory bottlenecks.
To ensure maximum efficiency, you can manage NVIDIA Deep Learning GPUs with Run:AI. Instead of manually allocating and provisioning resources, you can leverage automated and dynamic resource management.
Run:AI automates resource management and workload orchestration for machine learning infrastructure. With Run:AI, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:AI:
Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run.ai GPU virtualization platform.
We have authored in-depth guides on several other artificial intelligence infrastructure topics that can also be useful as you explore the world of deep learning GPUs.
In today’s highly competitive economy, enterprises are looking to Artificial Intelligence in general, and Machine and Deep Learning in particular, to transform big data into actionable insights that can help them better address their target audiences, improve their decision-making processes, and streamline their supply chains and production processes, to mention just a few of the many use cases out there. In order to stay ahead of the curve and capture the full value of ML, however, companies must strategically embrace MLOps.
See top articles in our MLOps guide:
This guide explains the Kubernetes Architecture for AI workloads and how K8s came to be used inside many companies. There are specific considerations implementing Kubernetes to orchestrate AI workloads. Finally, the guide addresses the shortcomings of Kubernetes when it comes to scheduling and orchestration of Deep Learning workloads and how you can address those shortfalls.
See top articles in our Kubernetes for AI guide: