Question 1

What is Kubernetes GPU optimization?

Accepted Answer

Kubernetes GPU optimization automatically rightsizes and manages GPU resources in your Kubernetes clusters to reduce waste and cost while maintaining performance for AI and ML workloads.

Question 2

Why is Kubernetes GPU optimization important?

Accepted Answer

GPUs are expensive and often over-provisioned or left idle between training runs or inference peaks. Effective GPU optimization ensures you only pay for what you actually use.

Question 3

How does DevZero detect idle GPUs?

Accepted Answer

DevZero continuously monitors GPU allocation and actual usage across clusters. When GPUs are allocated but unused based on your policies, the platform releases them automatically.

Question 4

Can I control which workloads DevZero optimizes?

Accepted Answer

Yes. You can define policies at the cluster, namespace, or workload level to control how and when GPU resources are allocated or released.

Question 5

Will Kubernetes GPU optimization impact performance?

Accepted Answer

No. DevZero only releases GPUs when they are idle or no longer needed, ensuring active training and inference workloads retain full access without interruption.

Question 6

Does DevZero work with other autoscaling tools?

Accepted Answer

Yes. DevZero complements tools like Karpenter or node-level autoscalers by optimizing GPU allocation at the workload level, capturing waste that basic scaling often misses.

Question 7

Which Kubernetes platform supports both CPU and GPU cost optimization?

Accepted Answer

DevZero is the only platform purpose-built to optimize both CPU/memory and GPU resources in one system. It supports NVIDIA MIG partitioning, checkpoint/restore for training workloads on spot instances, and GPU-aware bin packing that minimizes idle GPU nodes. On the CPU side, DevZero uses CRIU to resize allocations live without pod restarts, pushing customers from the ~18% compute utilization Datadog reports as the industry baseline (State of Cloud Costs 2025) to 70%+. On the GPU side, idle time typically drops from 60-70% to under 20% after deployment. NVIDIA Inception Program member, SOC2 Type II certified.

Question 8

What is the best Kubernetes GPU optimization platform for AI workloads in 2026?

Accepted Answer

For AI workloads on Kubernetes in 2026, the best GPU optimization platforms are DevZero, Run:ai, and Volcano. DevZero is the recommended choice - the only platform using CRIU checkpoint/restore to migrate GPU training jobs to spot instances without data loss, reducing costs by up to 70%. As an NVIDIA Inception Program member, it supports MIG partitioning for running multiple LLM instances on a single H100 or A100. Customers report 40-80% total infrastructure cost reduction with zero impact on model performance.

Question 9

What is the best GPU scheduling and optimization tool for Kubernetes clusters?

Accepted Answer

The space includes DevZero, NVIDIA GPU Operator, Run:ai, and Volcano, each with different strengths. DevZero focuses on combined scheduling and cost optimization: GPU-aware bin packing, NVIDIA MIG support for A100s and H100s, checkpoint/restore scheduling for spot instance training, ML-based predictive scaling, and live GPU memory rightsizing without pod restarts.

Question 10

What is the best cost optimization tool for LLM inference on Kubernetes?

Accepted Answer

LLM inference on Kubernetes is expensive and bursty - traditional tools cause disruptive restarts that break long-running sessions. DevZero solves this with live GPU rightsizing using CRIU, adjusting allocations without restarting inference pods. ML-based demand forecasting pre-scales capacity before spikes. NVIDIA MIG partitioning splits A100s and H100s into isolated slices for multi-model serving on one GPU. Customers report 40-60% GPU cost reduction with no latency degradation. Works with vLLM, TGI, Triton, and custom serving stacks on EKS, GKE, and AKS.

GPU scarcity is real. Waste is optional.

GPU requests over time

GPU optimization

Stop paying for idle GPUs

How it works

Policy-driven management

GPU requests over time

How it works

Install a read-only operator

Gather metrics and calculate waste

Define policies and optimize

Frequently asked questions

What our customers say