As enterprise AI teams move beyond training and into real-time production inference, they’re encountering a fundamental infrastructure problem: GPU resource management built for training doesn’t scale to inference.

Run:AI is one of the most popular platforms for GPU orchestration in Kubernetes environments. It excels in statically partitioning GPU workloads within a single cluster. But inference is not training—and the infrastructure requirements for inference are vastly different.

Inference needs to be dynamic, elastic, and resilient across clusters and cloud environments. This is where Avesha’s Elastic GPU Service (EGS)—especially with its recent v1.12.0 release—offers critical advantages through innovations like GPU Provisioning Requests (GPRs).

The Inference Problem: Why Run:AI Isn’t Enough

 While Run:AI provides solid GPU scheduling and fair-share capabilities, it comes with notable limitations when applied to real-time, scalable inference:

1. No Native Cloud Bursting If your cluster is full, Run:AI doesn’t automatically extend workloads to other clusters or cloud GPUs. You either wait, overprovision, or re-architect. 
2. Lack of Multi-Cluster Awareness Run:AI is designed for single-cluster operation. Inference pipelines often need distributed clusters across regions, clouds, and edge locations.
3. No Built-In Failover or Recovery When a GPU node fails, inference jobs stall. There’s no intelligent rerun, hot standby, or automatic fallback.
4. No Elastic Cost Optimization Run:AI lacks dynamic selection of spot or low-cost GPU instances based on workload profiles. This leads to waste during peak demand. 
5. No Lightweight Fallback Support If GPUs aren’t available, Run:AI doesn’t provide automatic CPU fallback for latency-tolerant inference jobs.

 These limitations hinder enterprise teams trying to operationalize inference reliably and cost-efficiently. 

Augmenting Run:AI with Avesha EGS

Avesha EGS is designed to augment existing GPU orchestration platforms like Run:AI—not replace them.

Where Run:AI manages in-cluster fairness and scheduling, EGS provides the next layer: cross-cluster elasticity, job resilience, and cost-efficient cloud bursting. 

EGS introduces the following core capabilities: 

• Proactive failure detection – Real-time GPU health monitoring prevents cascading issues.
• Inference job reruns – Built-in retry logic avoids restarts and loss of progress.
• Cross-region failover – Seamless GPU provisioning across multi-region environments. 
• Hot standby GPUs – Keep capacity on standby to minimize inference downtime. 
• Hybrid GPU-CPU fallback – Automatically reroute lightweight inference jobs to CPUs when GPUs are exhausted.

Introducing GPRs: GPU Provisioning Requests 

At the heart of this capability is EGS’s GPR mechanism. In the latest release (v1.12.0), Avesha introduced GPR Templates—a declarative, reusable way to request GPU capacity from federated infrastructure.

• A GPR (GPU Request) defines:
• The required GPU class (e.g., A100, L4, T4)
• Allocation policies (burst permission, scope, fallback)
• Time-to-live for the request
• Priority and workload class handling

 Instead of manually intervening when GPU capacity is tight, inference services can declare their intent, and EGS provisions what’s needed—across on-prem, cloud, or edge clusters. 

Why Cloud Bursting Matters for Inference 

Inference workloads are real-time, unpredictable, and driven by user demand. They can spike without warning, requiring infrastructure to react instantly. 

EGS allows inference services to:

•  Burst seamlessly to public cloud when local GPUs are full
•  Automatically clone namespaces, secrets, and volumes across clusters
•  Select the most cost-effective GPU instances based on workload profiles
•  Define granular policies for tenancy, isolation, and failover handling

This allows enterprises to support inference pipelines that are resilient, scalable, and budget-aware, without requiring application rewrites.

 Example: Augmenting Run:AI for Production Inference

Let’s say your inference pipeline is running in a Run:AI-managed Kubernetes cluster. During a traffic surge, the cluster runs out of GPU capacity.

Avesha’s EGS detects pressure in the workload queue. It generates a GPR (GPU Request), which dynamically provisions GPU resources in a federated Nebius or public cloud cluster. The inference workload is cloned and scheduled there automatically. No downtime. No developer intervention. 

If the primary GPU node fails, EGS reruns the job on a standby GPU or reroutes it to a CPU, depending on defined thresholds.

Proven Results with EGS

 In production deployments, Avesha’s EGS has helped organizations:

• Increase GPU utilization by over 45% across hybrid environments
• Reduce inference queue latency by 30–40%
• Lower cloud GPU spend by up to 28% using spot-aware bursting

Conclusion

Run:AI solves the static GPU scheduling problem within Kubernetes clusters. But AI inference at scale is not static. It’s bursty, cross-cluster, and SLA-sensitive.

Avesha’s Elastic GPU Service complements Run:AI by enabling elastic, resilient, and cost-efficient inference infrastructure—without requiring developers to change their application architecture.

 If your inference workloads are stuck in a single cluster, it’s time to evolve. Explore Avesha EGS and GPU Requests (GPRs): Elastic GPU Service Overview EGS 1.12.0 Release Notes GPR (GPU Request) API Reference

Explore Avesha EGS and GPU Requests (GPRs):

• Elastic GPU Service Overview
• EGS 1.12.0 Release Notes
• GPR (GPU Request) API Reference