As organizations continue to invest billions in GPU infrastructure for AI workloads, a critical gap has emerged in monitoring and observability capabilities. Neurox, a new self-hosted platform, aims to solve this problem by providing comprehensive GPU monitoring specifically designed for Kubernetes environments.
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| This screenshot shows the GitHub repository for the Neurox Control Helm Chart, which supports GPU monitoring in Kubernetes environments |
The GPU Observability Problem
The rapid growth in AI infrastructure has exposed significant limitations in existing monitoring solutions. According to discussions in the tech community, current tools fail to answer fundamental questions about GPU utilization, ownership, and costs. Traditional metrics like DCGM_FI_DEV_GPU_UTIL can show what's happening with GPUs but not why - leaving teams unable to diagnose issues like underutilized resources, misconfigured applications, or jobs silently falling back to CPU processing.
GPU observability is broken... Despite companies throwing billions at GPUs, there's no easy way to answer basic questions: What's happening with my GPUs? Who's using them? How much is this project costing me?
Most organizations are currently cobbling together solutions using Prometheus, Grafana, and kubectl scripts, creating a fragmented view of their GPU infrastructure. This approach falls short when teams need to understand the relationship between metrics, Kubernetes state, and financial data across multi-cloud environments.
Neurox's Approach to GPU Monitoring
Neurox combines three critical data sources to provide comprehensive observability: GPU runtime statistics from NVIDIA SMI, running pod information from Kubernetes state, and node data with events from Kubernetes state. This integration allows teams to trace issues like failed pod states, incorrect scheduling, and applications not properly utilizing GPU resources.
The platform offers purpose-built dashboards for different roles within an organization. Researchers can monitor workloads from creation to completion on the Workloads screen, while finance teams can access cost data grouped by team or project on the Reports screen. This role-based approach addresses the diverse needs of administrators, developers, researchers, and finance auditors working with GPU infrastructure.
Deployment Flexibility and Data Privacy
A key aspect of Neurox's architecture is its separation between control plane and workload components. The platform is designed as self-hosted software to keep sensitive data within an organization's infrastructure. For teams with limited storage on GPU clusters, Neurox offers a split deployment model - the control plane can be installed on any Kubernetes cluster with persistent storage (like EKS, AKS, or GKE), while only the lightweight workload agent needs to run on GPU clusters.
This flexibility addresses concerns about the 120GB persistent storage requirement mentioned in the documentation, making the solution viable for bare metal GPU clusters with limited local storage. The architecture also potentially allows for future cloud-hosted control plane options while keeping workload data secure.
Neurox offers a free tier for monitoring up to 64 GPUs, which covers many personal, academic, and light commercial use cases. While not currently open-source, the company has indicated they're considering this path for the future, recognizing that privacy and cost concerns drive interest in open-source alternatives.
As AI infrastructure continues to grow in complexity and scale across multi-cloud environments, purpose-built observability tools like Neurox may become increasingly important for organizations looking to optimize their substantial GPU investments.
Reference: Neurox Control Helm Chart