NVIDIA GPUs are designed to accelerate AI workloads, providing a number of benefits over CPUs, including: Higher performance, More efficient, Scalability and Versatility

NVIDIA and AMD are both leading manufacturers of GPUs, which are used for a variety of tasks, including AI, machine learning and Deep Learning workloads. NVIDIA GPUs are generally considered to be better for AI workloads than AMD GPUs, due to their higher performance and better support for AI libraries and frameworks. However, AMD GPUs are becoming more competitive in the AI market, and they may be a good option for budget-conscious users.

Any business dealing with video and audio content production, streaming, and distribution would agree that video transcoding is a crucial aspect of their operations. The process of converting media content from one format to another can take up a lot of time and resources. Fortunately, thanks to the latest technological advancements, transcoding can now be done with ease and efficiency using GPUs.

JedAI provides a collaborative development environment for Data Science teams to experiment, train, fine-tune, and deploy LLMs or GenAI workloads across the enterprise and in the Cloud, on NVIDIA® powered GPUs.

SuperMicro offers a wide range of GPU servers that are designed for high performance and reliability. If you are looking for a high-performance, reliable, and scalable GPU server, SuperMicro is a great option.

The specifications for GPU cards can be long and hard to absorb, we try and make it easy for users, especially new users, to understand.

NVIDIA released the H100 last year which has already evolved into the market-leading enterprise GPU, being deployed in endless amounts of servers worldwide.