Supporting a broad spectrum of Reasoning & Inferencing use cases requires smart GPU orchestration via intelligent scaling and GPU allocation– Maximize ROI of GPU investments - Maximize ROI of GPU investments

The demand for high-performance AI inference and training continues to skyrocket, placing immense pressure on cloud and GPU infrastructure. AI models are getting larger, and workloads are more complex, making efficient resource utilization a critical factor in cost and performance optimization. Enter Avesha Smart Scaler — a reinforcement learning-based scaling solution that dynamically optimizes GPU/CPU resource allocation for AI workloads, delivering unprecedented throughput gains and reduced inference latency.

KubeSlice provides a robust framework for securing Kubernetes environments by implementing logical slices that segment workloads, enforce network isolation, and integrate Zero Trust principles. This whitepaper explores the security features of KubeSlice, including role-based access control (RBAC), network segmentation, and encrypted communication across clusters.

Avesha’s Gen AI Smart Scaler is a next-generation Horizontal Pod Autoscaler (HPA) replacement that uses AI-driven predictive scaling to optimize pod readiness specifically for AI inferencing workloads. Unlike traditional reactive scaling, Smart Scaler anticipates demand patterns and scales pods proactively, dramatically improving throughput and reducing latency.

Despite advancements in ML scheduling tools like KubeFlow, optimizing GPU and CPU usage remains difficult. Mismatches between resource management and workload orchestration cause idle GPUs: creating delays, and inefficiencies in large-scale setups. Current GPU allocation relies on manual adjustment and lacks dynamic adaptation. Without standardized GPU rating and sharing approaches, advanced ML schedulers still struggle with scheduling, leading to bottlenecks and resource waste.

Kubernetes autoscaling is crucial for maintaining operational efficiency and maximizing cloud return on investment (ROI). However, fully automating autoscaling in Kubernetes to ensure applications accurately and efficiently drive their own scaling needs presents a significant challenge.

Customers can reduce the cost of nodes by ~ 56% when they introduce both, Karpenter for node auto scaling on EKS and replacing HPA with Smart Scaler for the pod autoscaling microservices. Karpenter simplifies Kubernetes infrastructure with the right nodes. Smart Scaler simplifies Kubernetes using GenAI to autoscale pod based on Application behavior and infrastructure metrics.

Effectively managing connectivity across multiple clouds and clusters has become a pivotal challenge for today’s enterprises. The complexities of network architecture, combined with the limitations of existing solutions like Cilium, Skupper, and Submariner, call for a robust, scalable, and user-friendly connectivity solution. Avesha KubeSlice addresses the connectivity needs of multicloud and multi-cluster environments, setting a new standard for efficient and secure networking.