Deploying AI Models at the Edge

StackPath launches dedicated hosts for edge compute

StackPath launches dedicated hosts for edge compute

Edge computing platform StackPath has announced the addition of dedicated hosts for its edge computing products.

Edge compute dedicated hosts lets a customer deploy StackPath Edge VMs or its edge containers hosted on a physical server. The company notes that these servers are dedicated to just that customer to ensure optimal and consistent performance.

Configuration and management of the virtual machine and container instances rely on the unified orchestration of StackPath EdgeEngine, in order to provide the same flexibility, observability, and control available across the StackPath platform via a single management portal and API.

“Businesses choose StackPath for the performance advantages we provide with edge computing compared to legacy public cloud. Dedicated Hosts give unparalleled consistency with ultra-low latency on unshared capacity physically close to end users,” says Tom Reyes, Chief Product Officer for StackPath.

“We are ensuring that sole-tenancy, noisy neighbors, compliance, and privacy requirements for cloud customers are easily addressed while the ease of use of our platform continues to be at the forefront.”

StackPath says its edge compute dedicated hosts feature a physically separate computing environment for enhanced data security and deterministic cloud performance to handle usage spikes, consistently high traffic, optimized end-user experiences, and privacy.

The edge compute instances are managed via the StackPath Customer Portal or API. Additional options include forming virtual private clouds and leveraging built-in L3-L4 DDoS protection, persistent storage, image capture and deployment, private IP addresses, and more.

Recently, StackPath added NVIDIA GPU-accelerated instances to its Virtual Machine (VM) and container product options.

The new instances use NVIDIA A2 Tensor Core and NVIDIA A16 GPUs to deliver the compute power required for workloads such as deep learning algorithms, graphical processing, and other parallel architectures.

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