Inference at the Edge
Easily deploy ML models at the edge
to achieve fast, secure, and scalable inference worldwide.
Revolutionize your AI applications with edge inference
Gcore brings inference closer to your users, reducing latency, enabling ultra-fast responses, and facilitating real-time AI-enabled apps.
Use a single endpoint automatically deployed where you need it and let Gcore manage the powerful underlying infrastructure for exceptional performance.
Why Gcore
Inference at the Edge?
High performance
Deliver fast AI applications with high throughput and ultra-low latency worldwide.
Scalable
Effortlessly deploy and scale cutting-edge AI applications across the globe.
Cost efficient
Automatically adjust resources based on demand, paying only for what you use.
Quick time-to-market
Accelerate AI development without infrastructure management, saving valuable engineering time.
Easy to use
Use an intuitive developer workflow for rapid and streamlined development and deployment.
Enterprise ready
Benefit from integrated security and local data processing to help ensure data privacy and sovereignty.
Join our Beta program for free
Experience Inference at the Edge and help shape its future with your feedback.
Join BetaWhile we refine the product for general availability, we advise against using it for mission-critical tasks or production environments.
Effortless model deployment from a single endpoint
Leave the complexities of GPUs and containers to us. Get started in three easy steps.
- 01
Model
Choose to build with leading foundational models or train your own custom models.
- 02
Location
Select a specific location or use Smart Routing to automatically deploy from the nearest edge location.
- 03
Deploy
Run your models securely at the edge with high throughput and ultra-low latency.
How Inference
at the Edge works
A globally distributed edge platform for lightning-fast inference
Run AI inference on our global network for real-time responses and exceptional user experiences. With 180+ points of presence in 90+ countries, your end users will experience lightning-fast inference, no matter where they are.
Unleash your
AI apps’ full potential
Low-latency global network
Accelerate model response time with over 180 strategically located edge PoPs and an average network latency of 30 ms.
Powerful GPU infrastructure
Boost model performance with NVIDIA L40S GPUs, designed for AI inference, available as dedicated instances or serverless endpoints.
Flexible model deployment
Run leading open-source models, fine-tune exclusive foundational models, or deploy your own custom models.
Model autoscaling
Scale up and down dynamically based on user requests and pay only for the compute you use.
Single endpoint for global inference
Integrate models into your applications and automate infrastructure management with ease.
Security and compliance
Benefit from integrated DDoS protection and compliance with GDPR, PCI DSS, and ISO/IEC 27001 standards.
A flexible solution
for diverse use cases
Technology
- Generative AI applications
- Chatbots and virtual assistants
- AI tools for software engineers
- Data augmentation
Gaming
- AI content and map generation
- Real-time AI bot customization and conversation
- Real-time player analytics
Media and Entertainment
- Content analysis
- Automated transcription
- Real-time translation
Retail
- Smart grocery with self-checkout and merchandising
- Content generation, predictions, and recommendations
- Virtual try-on
Automotive
- Rapid response for autonomous vehicles
- Advanced driver assistance
- Vehicle personalization
- Real-time traffic updates
Manufacturing
- Real-time defect detection in production pipelines
- Rapid response feedback
- VR/VX applications
Frequently
asked questions
AI inference is when a trained ML model makes predictions or decisions based on new, previously unseen data inputs. Inference applies an ML model to real-life issues, such as a new chat prompt, to provide useful insights or actions. Read our blog post to learn more about AI inference and how it works.
AI inference at the edge differs from cloud-based AI inference in terms of where data processing occurs. Edge AI inference involves running ML models on or near local devices, allowing real-time data analysis and decision-making without the need to send data to a remote server, as is the case with cloud AI inference.
Deployment of AI inference at the edge results in reduced latency, improved security, and decreased reliance on network connectivity compared to AI inference in the cloud. Inference at the edge is particularly useful for AI apps that need real-time processing and minimal delay, like generative AI and real-time object detection.
Yes. AIoT devices rely on ML models deployed at the edge. Gcore Inference at the Edge provides the low latency, high throughput, and close proximity to data sources that are essential for AIoT systems.
Gcore offers 5G Network, a solution specifically designed for IoT, including AIoT, that can be used in combination with Inference at the Edge. 5G Network is a secure, reliable, and fast way to connect remote AIoT devices over 5G. To learn more about 5G Network capabilities, explore our 5G Network Docs.
The NVIDIA L40S is the latest universal data center GPU that is specifically designed for AI inference. It delivers up to 5x faster inference performance compared to other powerful NVIDIA GPUs, such as the A100 and H100, and offers a superior price/performance ratio. Read our blog post to learn more about the L40S and how it differs from other popular NVIDIA GPUs.
Contact us to discuss your project
Get in touch with us and explore how Inference at the Edge can enhance your AI applications.
Talk to an expert
Try other Gcore products
GPU Cloud
Virtual Machines and Bare Metal with A100 and H100 NVIDIA GPUs for AI training and high-performance computing
Container as a Service
Serverless solution for running containerized applications and ML models in the cloud
Managed Kubernetes
Fully managed Kubernetes clusters with GPU worker node support for AI/ML workloads
FastEdge
Low-latency edge computing for deploying serverless applications
Object Storage
Scalable S3-compatible cloud storage for storing and retrieving data
Function as a Service
Serverless computing for running code in a prebuilt environment
