Podwide-InferX: Efficient and Scalable Inference
Fast Scalable Reliable
Podwide-InferX leverages the robust capabilities of Kubernetes and KubeRay to deliver fast spin-up times and dynamic autoscaling. Our service handles inference across thousands of GPUs, seamlessly adapting to changing demands with consistent high performance.
Customized Solutions for Every Need
Whether for public cloud or private deployment, Podwide-InferX offers tailored solutions to meet your specific requirements.
- Faster spin-up times
- Responsive autoscaling
- Efficient management of thousands of GPUs
- Seamless scalability to adapt to changing demands
- Global distribution and edge inference
- Private deployment options ensuring data security and compliance
Enhance Your Inference Capabilities
——— " Unmatched Performance. Minimal Latency."
Seamless Development
- Reduces friction with Ray's AI libraries
- Seamless scaling from laptop to large cluster
- Quick deployment and efficient operations
Unified API and Runtime
- Ray's API enables framework switching (XGBoost, PyTorch, Hugging Face)
- Unified runtime integrating Ray, KubeRay, and Kubeflow
- Simplified development process
Resource Management
- KubeRay on Kubernetes without hypervisor
- Precise GPU resource management with DMOS
- Real-time GPU monitoring for optimal performance
Open and Extensible
- Fully open-source, runs on any cluster, cloud, or Kubernetes environment
- Scalable developer APIs for custom components and integrations
Why Ray?
Real-world Applications and Testimonials for Full-Stack Training and Inference Services
Ray is trusted by leading companies like OpenAI, Uber, and Ant Group for its practical benefits in full-stack training and inference services:
OpenAI: Uses Ray to train large models like ChatGPT, highlighting Ray's ability to accelerate iteration at scale and provide efficient inference.
Uber: Adopted Ray as the unified compute backend, improving model training efficiency and reducing the complexity of their deep learning platform.
Ant Group: Deployed Ray Serve on a massive scale during the world's largest online shopping day, achieving unprecedented transaction throughput for inference services.
Ray delivers exceptional model training efficiency, low latency, and high fault tolerance in full-stack training and inference services, making it the ideal choice for building scalable and reliable distributed systems.
Technical Architecture
Podwide-InferX Tech Stack
- Based on Kubernetes running KubeRay
- No hypervisor layer; K8s runs directly on bare metal
- Easy to scale; instance spin-up time is measured in seconds
Autoscaling Efficiency
- Small models: 5 seconds
- Larger models: 30-60 seconds
Serverless Kubernetes
KServe enables serverless inferencing on Kubernetes, supporting common ML frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX, simplifying the model deployment process.
Networking and Storage
- High-performance Kubernetes native network design
- Access via multiple global Tier 1 providers up to 100Gbps per node
- Custom configuration with Podwide-InferX Virtual Private Cloud (VPC)
- Scalable storage solutions built on Ceph
- Supports S3-compatible object storage, HTTP, and Podwide-InferX Storage Volumes
Cost Efficiency
Podwide-InferX optimizes GPU usage and autoscaling to reduce costs. Running Kubernetes directly on bare metal ensures higher speed and performance.
Scalable:Spin up thousands of GPUs in seconds and scale to zero during idle times, consuming no resources and incurring no costs
Cost-Effective:No fees for ingress, egress, or API calls; pay only for the resources you use.
Use Cases
Independent Use of Ray Libraries
Users can independently use Ray's AI libraries for specific AI applications or services. For example, use RLlib to train models or Ray Serve to deploy model pipelines without integrating with existing ML platforms.
Integration with Existing ML Platforms
Podwide-InferX integrates with existing pipeline/workflow orchestrators, storage, and tracking services to complement existing ML platforms without replacing them. Use Ray within ML platforms like SageMaker or Vertex.
LLM Application Development Platform
Supports LLM application development, enabling enterprises to efficiently develop and manage generative AI applications. Leveraging the Dify platform, it provides support for hundreds of models, an intuitive prompt orchestration interface, high-quality RAG engines, and a flexible agent framework.
AI Agents
Supports the development and deployment of AI agents. Utilizing its powerful computing capabilities and flexible resource scheduling, developers can create intelligent AI agents for automating tasks, customer service, intelligent Q&A, and more. These agents can be managed and optimized through the Dify platform, ensuring efficient operation and meeting business needs.
Edge Inference
Edge inference services leverage Podwide's distributed cloud mechanism to deploy inference clusters near the network edge globally, providing low-latency, high-performance AI inference services.
- Real-time AI Applications: Suitable for applications requiring quick responses, such as intelligent customer service and real-time translation.
- Edge Devices: Supports running inference models on edge devices, reducing data transmission latency.
- Personalized Recommendations:Generates personalized content in real-time based on user location and behavior data.
- Internet of Things (IoT) Applications: Processes data at edge nodes to reduce the burden on cloud computing, improving response speed and efficiency.
Private Deployment Solutions
Podwide-InferX offers highly customizable private deployment solutions, ensuring your data security and compliance. Our team works closely with you to design and implement an inference service architecture tailored to your business needs, maximizing efficiency and performance.
Get Started with Podwide-InferX
Experience the power of Podwide-InferX and enhance the efficiency and scalability of your AI inference services. Learn more and contact our team to help you achieve your business goals.