TL;DR:
- StackPath introduces NVIDIA GPU-Accelerated Instances for VMs and Containers.
- These instances leverage NVIDIA A2 and A16 GPUs for advanced computing.
- Targeted workloads include deep learning, high-end graphics, and parallel architectures.
- Accessible in key locations with high data transit efficiency.
- Offers low latency for real-time applications.
- Multiple configurations are available.
- On-demand provisioning and hourly billing.
- Supplementary features include DDoS protection and private IP addresses.
Main AI News:
StackPath, the renowned leader in the realm of edge computing, has proudly unveiled its latest offering – NVIDIA GPU-Accelerated Instances, a formidable addition to its arsenal of Virtual Machine (VM) and Container product options. These cutting-edge instances harness the prowess of NVIDIA A2 Tensor Core and NVIDIA A16 GPUs, setting the stage for a new era in computational supremacy. They are poised to cater to the insatiable appetite of workloads that demand exceptional computational might, including deep learning algorithms, high-intensity graphical processing, and various parallel architectures. These capabilities are not mere luxuries; they are the driving force behind revolutionary technologies spanning artificial intelligence (AI), machine learning (ML), augmented reality (AR), and virtual reality (VR).
As the curtain rises on this groundbreaking launch, StackPath ensures that its GPU-Accelerated Instances are accessible in strategic locations such as Dallas, San Jose, and Frankfurt, with plans for further expansion across the StackPath platform throughout 2024. This strategic positioning near data sources and destinations empowers these instances with a unique blend of computational potency and data transit efficiency.
Tom Reyes, Chief Product Officer for StackPath, underlines the significance of this development, stating, “Our GPU-Accelerated Instances are tailor-made for the demands of cutting-edge workloads like AI inference, computer vision, and natural language processing. These applications thrive on real-time responsiveness, necessitating not only high computational power but also exceptionally low latency. StackPath’s geographical proximity to data sources minimizes the data’s journey, ensuring that the advantages offered by GPU acceleration remain uncompromised by geographic distances.”
Configurations That Define Excellence
StackPath’s SP// Edge Compute VM GPU-Accelerated Instances are available in configurations designed to meet a spectrum of needs:
- 1 NVIDIA A2/16 GPU x 12vCPUs x 48GiB RAM x 25GiB Root Disk
- 2 NVIDIA A2/16 GPU x 24vCPUs x 96GiB RAM x 25GiB Root Disk
- 4 NVIDIA A2/16 GPU x 48vCPUs x 192GiB RAM x 25GiB Root Disk
For those seeking container-based solutions, the SP// Edge Compute Container GPU-Accelerated Instances offer an array of options:
- 1 NVIDIA A2/16 GPU x 12vCPUs x 48GiB RAM x 40GiB Root Disk
- 2 NVIDIA A2/16 GPU x 24vCPUs x 96GiB RAM x 40GiB Root Disk
- 4 NVIDIA A2/16 GPU x 48vCPUs x 192GiB RAM x 40GiB Root Disk
Seamless Access, Hourly Billing, and Beyond
StackPath ensures that these state-of-the-art edge compute instances are accessible at your fingertips through the StackPath Customer Portal or API. The billing model is designed with flexibility in mind, with charges based on both the usage duration and data transferred. Additionally, users can take advantage of supplementary features, including the creation of virtual private clouds, harnessing built-in L3-L4 DDoS protection, securing persistent storage, facilitating image capture and deployment, and configuring private IP addresses.
Conclusion:
The launch of StackPath’s NVIDIA GPU-Accelerated Instances signifies a significant advancement in edge computing capabilities. With a focus on real-time, data-intensive applications like AI and VR, these instances provide the computational power and low latency necessary for cutting-edge technologies. This development is poised to drive innovation and efficiency in the market, making StackPath a formidable player in the evolving landscape of edge computing.
