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GPU sharing

Kubernetes Wasn't Built for GPUs. Make It Behave

Kubernetes Wasn’t Built for GPUs. Make It Behave

Kubernetes counts whole GPUs and treats pods as disposable. An LLM pod is neither. Share the silicon with MIG/MPS/time-slicing and stop paying for idle ...

Sneha Gullapalli | August 12, 2026 | A100, AI infrastructure, AI Workloads, cloud native AI, Dynamic Resource Allocation, GPU autoscaling, GPU cost reduction, GPU optimization, GPU partitioning, GPU sharing, GPU time-slicing, GPU utilization, H100, Karpenter, KServe, Kubernetes DRA, Kubernetes GPU scheduling, LLM Inference, model caching, multi-instance GPU, NVIDIA GPU Operator, NVIDIA MIG, NVIDIA MPS, scale-to-zero, VRAM

Stop Treating GPUs Like Web Pods

Stop Treating GPUs Like Web Pods

Kubernetes schedules accelerators as opaque integers, and your bill pays for it. Share the silicon, scale on the right signal and keep weights out of the image ...

Veera Ravindra Divi | August 12, 2026 | AI infrastructure, AI serving, autoscaling, cloud costs, cloud native AI, DCGM exporter, DRA, Dynamic Resource Allocation, GPU costs, GPU scheduling, GPU sharing, GPU utilization, GPUs, inference workloads, KEDA, kubernetes, Kubernetes GPU scheduling, LLM Inference, MIG, model weights, MPS, NVIDIA GPUs, NVIDIA MIG, Prometheus, scale-to-zero, time-slicing

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Migrating Apache Solr Workloads to Amazon OpenSearch Service

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Modern Software Development and Delivery

1Q1

2Q2

3Q3

4Q4

5Q5

6Q6

How would you best describe your organization's current software delivery environment(s) on mainframe computers? (Select all that apply)(Required)

Primarily manual or legacy delivery processes

Automated CI/CD in limited areas

Hybrid environment with multiple delivery platforms and toolchains

Mostly standardized CI/CD platform

Intelligent, governed software delivery platform

My organization does not utilize mainframes

Not sure / don't know

Which outcomes are most important to your organization's software delivery strategy today? (Select up to three)(Required)

Accelerating software delivery

Automating build, test, and deployment workflows

Integrating security and compliance into delivery

Providing governance and auditability

Supporting AI-assisted software delivery

Improving software supply chain visibility

Standardizing delivery across teams

Supporting both modern and legacy application delivery

Other

Not sure / don't know

What, if anything, is limiting your organization's mainframe software delivery progress? (Select up to three)(Required)

Legacy applications or legacy development processes

Fragmented tools and disconnected workflows

Security, governance, or compliance requirements

Limited automation across the delivery lifecycle

Difficulty integrating AI into software delivery workflows

Skills or resource constraints

Organizational or cultural resistance to change

Limited visibility across the software delivery lifecycle

Budget or investment constraints

Other

Nothing is significantly limiting our progress

Not sure / don't know

In which areas of your mainframe software delivery environment are you currently using AI? (Select all that apply)(Required)

Code generation / developer assistance

Automated testing / test generation

Build and pipeline optimization

Deployment automation

Monitoring and incident response

Security / compliance automation

Other

We are not currently using AI in our environment today

As software delivery responsibilities expand beyond traditional build, test, and deploy, which of the following areas is the most challenging for your organization today with respect to mainframe software delivery? (Select one)(Required)

Integrating security throughout the software delivery lifecycle

Meeting governance, audit, or compliance requirements

Managing software supply chain integrity and visibility

Operationalizing AI across software delivery workflows

Coordinating software delivery across diverse tools, teams, and environments

Other

None of these are significant challenges

Not sure / don't know

Which of the following do you expect is most likely to accelerate your organization's mainframe software delivery progress over the next 12-18 months? (Select one)(Required)

Greater automation across the software delivery lifecycle

Broader use of AI-assisted software delivery workflows

Stronger governance, policy, and compliance automation

Improved software supply chain visibility

Better integration across software delivery tools and platforms

Greater standardization across development teams

Other

No significant changes are planned

Not sure / don't know

Δ