Get more GPU capacity across regions when your AI applications need it. Seamless and automated bursting of AI and inference workloads. Dynamic GPU resource selection across data centers and clouds. Real-time orchestration with intelligent p lacement based on availability, proximity, and cost.

Innovating AI Infrastructure by Automating GPU Workload Expansion

Artificial intelligence doesn’t stop being challenging once a model is trained. In fact, that’s just the beginning. Most of the complexity and cost happens during inference—when models are used in real-time to power apps and services. Training is just 10 % of the journey; 90 % of AI’s cost, complexity, and business value lives in live inference.

EGS: The Digital Twin for AI Inference Runtime 

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.

Scaling AI Workloads Smarter: How Avesha's Smart Scaler Delivers Up to 3x Performance Gains over Traditional HPA

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.

Scaling AI Workloads Smart: How Avesha's Smart Scaler Delivers Up to 3x Performance Gains over Traditional HPA

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Slash AI Costs & Maximize GPU Efficiency with EGS | Optimize Your AI Workloads

When DeepSeek’s trillion-parameter Mixture of Experts (MoE) model processes a query, it doesn’t brute-force its way through every neuron. Instead, it dynamically activates only the specialized “experts” needed for the task—a vision model for images, a reasoning engine for logic, or a language specialist for translation.

IRaaS: The Silent Revolution Powering DeepSeek’s MoE and the Future of Adaptive AI

Unlock the true potential of AI with our Inferencing-as-a Service platform. Deploy AI models at scale with ease and efficiency. Our solution is designed to tackle the growing demands of AI inference workloads.

Inference and Reasoning-as-a-Service

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How Avesha EGS Enhances Run:AI

EGS: GPU Dynamic Resource Allocation

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EGS: Dynamic GPU Orchestration

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EGS: Detailed Video

At Elastic GPU Services (EGS), we’re redefining how organizations harness the power of GPU-intensive workloads. With EGS, observability, orchestration, and automation work in unison to unlock unparalleled efficiency, scalability, and cost-effectiveness—all tailored for AI, ML, and high-performance computing. AI, ML, and high-performance computing.

Transforming your GPU infrastructure into a competitive advantage

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.

Elastic GPU Service (EGS) - Workload Automation, Optimization, Cost Reduction, and Observability

EGS (Elastic GPU Service) optimizes GPU infrastructure for AI engineers by providing usage optimization, observability with real-time clarity, smart orchestration and automation. It redefines how organizations harness the power of GPU-intensive workloads. EGS automation unlocks unparalleled efficiency, scalability, and cost-effectiveness—all tailored for AI, ML, and high-performance computing.

EGS: One Pager

EGS integrates observability, orchestration, and cost optimization for GPUs, seamlessly combining these capabilities through automation to deliver significant business value.

Elastic GPU Service Making MLOPs Easier

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EGS: AI Health metrics tab (Power, Energy)

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Service Connectivity Layer for managing fleet of clusters for better application performance

Multi - cluster chargeback by application and teams

Service gateway for multi-cloud applications

Enables creation of a virtual cluster that allows pods to be directly interconnected across distributed clusters.

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Reduce your cloud costs from 20-70% with continuous predictive autoscaling of Kubernetes resources driven by AI

Single/Multi-cluster and multi-cloud GPU provisioning and management platform

Elastic GPU Service

KubeAgents adds intelligence and autonomy to Kubernetes

KubeSlice Enterprise released version 1.16.0

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Elastic GPU Service released version 1.14.0

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