Choosing the Right GPU for Data Science
The sheer amount of data the world consumes in a single day exceed far more than we can imagine. As data scientists work on projects that require rationalizing the data ingest, millions upon millions of data points need to be organized and analyzed some way… But we don’t want to just sit around and read data line by line to prepare the data before we can use it effectively for tasks like analytics, machine learning, AI, and more.
What Data Science Solves?
Data science is the field of study using scientific methods and processes to combine domain, programming, and mathematics expertise in order to pull insights from raw data. It's about obtaining, processing, organizing, and analyzing data to gather insights, structure, and feed into other algorithms.
These data science insights can be used by machine learning algorithms in order to train artificial intelligence systems. which can perform human like tasks. This can include image recognition, fraud detection, generative AI, and more. Transforming raw, unprocessed data into AI systems is the main goal for Data Science – to generate usable data for analysts, scientists, and business users alike to convert into tangible value.
This all comes at the cost of compute. It is known that GPUs have been the choice accelerator for powering AI and data science due to its parallelized nature. But how can we pick from the various GeForce, RTX, H100, A100, Instinct, Radeon?
What to Look for in a GPU for Data Science
Data science requires intense computational requirements, and having the right GPU for data science computations will make everything much easier. And more often than not, data scientists now are following up their workload with training AI. But what features are important for considering what GPU to buy for data science and AI? There are several specifications that are crucial to look at for optimal performance, and aspects that chip makers and GPU manufacturers aim to increase, optimize, and improve:
CUDA Cores (NVIDIA) or Stream Processors (AMD)
The number of cores represent the number of parallel processing units inside a GPU. The more cores, the more parallelized computing it can perform at a given time. That is why the development of higher performance GPUs involves the shrinking of fabrication to increase the core density. Look for a GPU or accelerator that has a high number of Cores.
GPU Clock Frequency
Alongside cores, is the speed at which these cores can operate at. If the clock speeds (denoted by GHz) are low, these cores won’t perform as well as if it were to have a high clock speed. Look for high clock speeds, with consideration to the core count. More cores at slightly lower clock speeds could perform better than less cores with higher clock speeds, just like in CPUs. Think of it like an assembly line of 100 average speed workers versus an assembly line of 10 fast workers; if 100 average workers are used optimally, it will end up faster. However, for GPUs with roughly the same core counts, higher clock speeds will always perform better.
GPU Memory Size and Speed
The memory size of a GPU can greatly define the size of model a GPU can house and handle with less bottlenecks. In data science, bottlenecks are prevalent when the size of the model exceeds its VRAM, causing it to need to request additional data from storage. Alongside size is the speed at which the GPU can fetch data if needed. This is called bandwidth and the holy grail of GPUs for data science and deep learning is HBM (or high-bandwidth memory) which is significantly faster (but more expensive) than traditional GDDR memory.
FP16, FP32, and FP64 Performance
FP stands for floating point, which is the way computers define numbers and accuracy, learn more on this blog. In short, when GPU manufacturers talk about Floating Point Performance, they measure how fast these GPUs can calculate a set of instructions using more binary digits or less binary digits (represent a large decimal or real number). FP16 and FP32 are the more commonly used formats with sufficient accuracy using 16 and 32 binary digits to represent a value, whereas FP64 is highly accurate and used in more niche operations where extreme accuracy is essential. Identifying the required or recommended floating point accuracy and identifying the GPU with the best performance can help determine the GPU for your needs.
Power and Thermals
Considering these things is the power and thermals and the performance a GPUs can pull at those targets. The more a GPU can be cooled, the higher the cores can clock, the more heat and power will be used. Knowing your power targets and achieving optimal performance with regards to temperature, can optimize how your GPU performs for any application, not just data science.
Which GPUs are Best for Data Science?
NVIDIA is definitely at the top of the industry for providing data science, deep learning, and machine learning graphics cards. However, AMD is starting creep up and take the AI and data science hardware segment by storm. Here’s a short list:
- NVIDIA H100
- AMD MI300X
- NVIDIA L40S
- RTX 6000 Ada
- NVIDIA RTX 4090/4080/3090
NVIDIA H100 for Data Science and AI
The NVIDIA H100 SXM5 is a passively cooled socketed data center GPU with 16,896 CUDA Cores and 80GB of HBM3 memory and is the grail of data science and deep learning GPUs. This is overkill for any data science enthusiast, but for enterprise applications where ingesting massive amounts of data is paramount, an S-tier accelerator is essential for running a smooth operation.
Here is an NVIDIA HGX H100 dual Intel Xeon Platform and an NVIDIA HGX H100 dual AMD EPYC Platform features 8 H100 SXM5 GPUs on an NVLinked baseboard.
AMD Instinct MI300 for Data Science and AI
The AMD Instinct MI300X series accelerators are also OAM socketed GPUs with 19,456 stream processors and 192GB HBM3 memory and is the newly released accelerator and is putting AMD on the map. A lot of enterprise and researchers are interested in its overall performance, and is also overkill for enthusiasts, but has made ripples in the HPC space for powering next generation’s AI.
Check out this AMD Instinct MI300X platform coupled with dual AMD EPYC Processors.
There is also AMD’s newest innovation, the MI300A APU which places the CPU and GPU on the same die, similar to a consumer CPU with integrated graphics. However, its miles more advances housing a shared HBM memory of RAM and VRAM together to reduce the interconnectivity. bottlenecking.
NVIDIA L40S, RTX 6000 Ada for Data Science and AI
The NVIDIA L40S or RTX 6000 Ada are two PCIe GPU with 18,176 CUDA Cores and 48GB of GDDR6 memory. L40S is passively cooled while RTX 6000 Ada is actively cooled. NVIDIA positions these GPU as top performer for crunching large amounts of data. 48GB is no slouch, perfect for small to medium businesses.
Checkout these Deep Learning AI Server Platforms that you can configure with RTX 6000 Ada or L40S and other GPUs!
NVIDIA RTX 4090, 4080, 3090 for Data Science and AI
GeForce RTX 4090, 4080, 3090 are gaming focused cards, but they also excel in productivity workloads and are perfect for enthusiast level data science. It has ample memory at 24GB and 16GB are respectable memory capacity and with high clock speeds, it can be fast.
With these recommendations, it's good to note that NVIDIA Data Center GPUs like the H100, L40S, and RTX 6000 Ada are scalable friendly with servers that can house up to 10 GPUs at a time. AMD Instinct MI300X have a predetermined GPU size and is only found in servers. Consumer GPUs like the 4090 are not permitted in servers and are only found in workstations.
Explore these AI Workstation Platforms that support NVIDIA RTX 6000 Ada, RTX 4090 or other RTX and GeForce cards.
Closing Thoughts
We hope this guide has been helpful in helping you choose the right graphics card for your data science projects and applications. If you’re looking for any video cards for your next project, we offer many GPUs in all sizes and for all budgets. If you have any questions on pricing for a component or a customized solution, contact us today!