NVIDIA has several datacenter grade GPUs in its portfolio. In today’s blog we take a look at each one and explain some of their benefits.
This GPU combines the power of the NVIDIA H100 Tensor Core GPU with the network capabilities of the NVIDIA ConnectX-7 smart network interface card. This card is designed around AI training and 5G processing at the edge.
The H100 is designed around scalability and performance. NVIDIA NVLink allows for 256 H100s to be connected for large workloads. This card is designed with AI & large language models in mind.
The A100 is multi-purpose in nature. It focuses on AI, data analytics and HPC. The NVIDIA Ampete architecture is focused around true data center workloads. NVIDIA claims and we’ve noticed that it can provide up to 20X higher performance than prior generations. This card comes in 40GB & 80GB memory versions. A100 has competed for the world’s fastest memory bandwidth.
This is an entry-level datacenter grade GPU that has a small footprint with regards to power. It has a low-profile Gen4 PCIe card and a small configurable thermal design power compatibility.
The A10 is a single-slot, compact GPU. This GPU has a wide workload variety, from VDI to AI. This is a highly flexible GPU.
The A16 was designed with VDI in mind. With remote working becoming ever popular, the usage of native PC’s was at the forefront of this GPUs design. Compared to the M10, you can get 2X the amount of user density. This GPU is quite niche in that it is affordable and has an extremely high level of performance for VDI.
NVIDIA A30 Tensor Core GPU is deemed by NVIDIA as “the most versatile mainstream compute GPU for AI inference and mainstream enterprise workloads”. Made with AI at scale in mind, the same compute resource can rapidly re-train AI models as well as accelerate high-performance computing applications.
The NVIDIA A40 has been designed to accelerate the most demanding visual computing workloads. From powerful VDI to extreme rendering, the price/performance ratio for this GPU is outstanding.
One of NVIDIAs most important GPU releases to date, especially with the AI era we are in. The NVIDIA Tensor Core GPU is the “world’s most powerful accelerator for deep learning, machine learning, high-performance computing (HPC), and graphics”. With NVIDIA Volta™, a single V100 Tensor Core GPU has the performance of 32 CPUs. The V100 won MLPerf, the first industry-wide AI benchmark.
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There are lots of GPU choices depending on your use case and use cases vary wildly. Whether you are a home user gaming or a datacenter trying to machine learn 24/7, the use cases vary.
GPUs face complex algorithm problems that equate to use cases we know such as AI, machine learning, 3D rendering and more. A CPU may be used for general computation but a GPU, with lots of cores compared to a CPU, is focused on specific tasks and as such, can perform them at a much faster rate.