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By Allison Foster
The explosion of data. Private LLMs. AI. Complex queries. Data loss prevention. Speed to insight.
These are just a few of the drivers of the increasing demand for enterprise GPU solutions. These use cases – and there are hundreds of similar ones – are becoming impossible to solve without an enterprise GPU solution.
We’ll explore the top 3 enterprise GPU providers, key benefits, what to look for when choosing the right provider for you, and much more.
An enterprise GPU is when a graphics processing unit – a powerful processor capable of handling parallel tasks, especially when it comes to performing complex calculations – is used by an enterprise to provide high performance computing power that’s reliable and scalable.
Typically, an enterprise GPU is used for tasks such as AI, machine learning, complex queries, data visualization, and virtual desktops.
Why are enterprise GPUs so popular, and why is this popularity only growing?
Enterprise GPUs offer several business benefits. But more than this, for many use cases enterprise GPU usage is “table stakes,” empowering organizations to position themselves for long-term growth and industry leadership.
Some of these benefits include:
Are all GPUs more or less equal? Certainly not. There are several key differences between enterprise GPUs and the consumer versions.
Enterprise GPUs are optimized for the enterprise environment, offering increased performance, reliability and scalability. This also includes being able to handle sustained heavy workloads, often with specialized cooling systems and increased durability.
Consumer GPUs on the other hand are built for general use, from gaming to light creative work. These GPUs prioritize high framerates and stunning graphics, and less for prolonged and intensive tasks.
The enterprise GPU market is dominated by a relatively small number of players. Among these are:
Nvidia dominates the enterprise GPU market with models like the A100 and H100 which are widely used in AI, machine learning, and high-performance computing. Nvidia pioneered the CUDA parallel computing platform, enabling developers to harness GPU acceleration for a wide range of applications. Other popular models include the L40, L4, the RTX series, T4, M4000, and V100.
AMD offers the Instinct series, including the MI300, designed for data center and AI workloads. AMD introduced the MI300A, the world’s first data center APU integrating CPU and GPU on a single package. Its MI300 series has remained popular, along with the MI250 utilizing CDNA 2 architecture.
Intel’s Data Center GPU Max series (previously Ponte Vecchio) targets HPC and AI applications. Intel also developed the Data Center GPU Max series, featuring a multi-tile architecture delivering high performance for HPC and AI workloads. Its Flex series is also used in an enterprise environment.
Choosing the enterprise GPU that makes sense for your main use case will depend on what elements of a GPU are most critical. Here, we’ve listed the main factors to pay attention to when evaluating an enterprise GPU, and typical use cases for each:
Practical implication: Determines the GPU’s ability to handle heavy computational tasks, such as AI training or scientific simulations.
Use case: Training large-scale machine learning models.
Example: The NVIDIA H100 Tensor Core GPU provides up to 60 teraflops of double-precision performance
Practical implication: Affects how much data the GPU can store and process at once, critical for large datasets.
Use case: Running high-resolution simulations or multi-instance inference on large AI models.
Example: The NVIDIA A100 Tensor Core GPU features 80 GB of HBM2e memory with a bandwidth of 2,039 GB/s
Practical implication: Specialized cores for accelerating AI-specific operations like matrix multiplications, important for deep learning workloads.
Use case: Accelerating inference for neural networks in AI-driven applications.
Example: The NVIDIA H100 Tensor Core GPU includes 4th-gen Tensor Cores, providing up to 1,000 teraflops of AI performance
Practical implication: Impacts operational costs and suitability for environments with power constraints, as well as impacting organizations’ sustainability goals.
Use case: Organizations with mandated sustainability requirements.
Example: The NVIDIA T4 Tensor Core GPU offers 65 teraflops of mixed-precision performance within a 70-watt power envelope
Practical implication: Enables sharing a single GPU across multiple users or tasks, optimizing resource utilization.
Use case: Supporting virtual desktop infrastructure (VDI) for enterprise environments.
Example: The NVIDIA A40 GPU supports NVIDIA Virtual GPU (vGPU) software, allowing multiple virtual machines to share GPU resources effectively
Practical implication: Balances the GPU’s capabilities with budget needs.
Use case: For example, relevant for a startup or a research lab with limited funding.
Example: The NVIDIA RTX 3060 offers 12 GB of GDDR6 memory and 13 shader teraflops, providing a balanced solution between cost and performance.
A: Use cases include complex queries on massive datasets, AI and machine learning, and high performance computing.
A: Yes. An enterprise GPU’s powerful processing capabilities mean it can significantly improve AI and ML performance.
A: Enterprise GPU performance will depend on the use case, the type of GPU used, and other factors. Industries like healthcare, retail, manufacturing and telecoms have all benefitted from enterprise GPU performance enhancement.
SQream is the world’s leading data and analytics acceleration platform, harnessing the power of GPU acceleration to provide a powerful enterprise GPU solution, and making it possible to get previously unattainable insights from data and compute-intensive workloads – including on data sets reaching into the petabytes. In fact for SQream, no dataset is too large, and no query is too complex, making it the perfect enterprise solution.
This enterprise GPU capability empowers organizations to derive actionable insights from their data, while dramatically shortening time-to-insight and slashing costs.
In implementing SQream, you’ll benefit from:
SQream isn’t necessarily a competitor to your current enterprise GPU provider; rather it’s a value-adding game-changer, offering the on-demand force to propel your organization into a long-term leadership position in your industry.
To learn more about how you can benefit from SQream’s impactful capabilities, set up a call here.
We explored the enterprise GPUs including benefits, leading providers, examples and more.
The key takeaways are that an enterprise GPU solution is now mission-critical in today’s business environment, especially given the large volumes of data and the race to harness this data; that there are a few leading providers of enterprise GPUs, and choosing your specific solution will depend on your unique needs; and finally, that SQream is a powerful way to take your enterprise GPU capabilities to the next level and gain a sustainable advantage over your competitors.
