Nebuly’s Nos Challenges Static GPU Allocation in Kubernetes Environments

As the scarcity of H100 and A100 processors persists, the inefficiency of static resource allocation in Kubernetes has become a primary cost driver for AI infrastructure. Nos, an open-source module developed by Nebuly, aims to address this utilization gap through real-time dynamic partitioning and elastic quota management.

The prevailing operational model for GPU orchestration within Kubernetes often relies on static allocation. In this paradigm, a container requests a specific number of GPUs—usually integers—and retains exclusive access to those resources regardless of actual compute consumption. This results in significant 'dark silicon,' where expensive hardware remains reserved but idle. Nos attempts to dismantle this rigidity by introducing a scheduler-aware module designed to maximize Kubernetes GPU utilization.

Dynamic Partitioning Mechanics

At the core of the Nos architecture is the capability to schedule requests for partial GPU Pods. Unlike standard Kubernetes schedulers that view a GPU as a monolithic unit, Nos treats the GPU as a divisible resource. The system performs partitioning automatically in real-time based on pending and running Pods in the cluster.

This approach targets the 'bin-packing' problem inherent in MLOps. If an inference workload requires only 40% of an A100’s memory or compute capacity, standard allocation wastes the remaining 60%. Nos theoretically allows that fragment to be reclaimed and allocated to a pending pod, thereby increasing density. While NVIDIA’s Multi-Instance GPU (MIG) technology offers hardware-level isolation for up to seven instances, it is generally rigid and limited to specific high-end SKUs (A100/H100). It remains to be clarified through further technical validation whether Nos relies on CUDA time-slicing (software isolation) or orchestrates MIG profiles dynamically, a distinction that significantly impacts workload isolation and performance guarantees.

Elastic Resource Quota Management

Beyond individual device partitioning, Nos addresses organizational inefficiencies through Elastic Resource Quota Management. In large enterprises, specific teams (namespaces) often hoard GPU quotas to ensure availability, preventing other teams from utilizing idle resources. Nos increases the number of Pods running on the cluster by allowing namespaces to borrow quotas of reserved resources from other namespaces.

This feature introduces a 'use-it-or-lose-it' dynamic to cluster management. If a namespace is not actively consuming its reserved quota, the scheduler can temporarily reallocate those resources to a namespace with pending demand. This logic suggests a move toward over-provisioning strategies similar to CPU management in virtualized environments, aiming to improve GPU utilization and reduce infrastructure costs.

Market Context and Competitive Landscape

The release of Nos arrives during a consolidation phase in the GPU orchestration market. The recent acquisition of Run:ai by NVIDIA highlighted the immense value placed on software layers that can optimize GPU efficiency. However, that acquisition also removed a primary independent vendor from the board, creating a vacuum for open-source alternatives.

Competitors like AWS Karpenter focus on general node autoscaling, and the Volcano scheduler offers batch scheduling capabilities, but neither explicitly specializes in the granular, real-time fractionalization of GPUs in the same manner as Nos claims. The primary limitation for adoption will likely center on hardware compatibility and integration complexity. If Nos relies heavily on software-level time-slicing, it may be suitable for inference and development workloads but less viable for heavy training jobs where memory bandwidth contention could degrade performance.

Strategic Implications

For infrastructure leaders, the value proposition of Nos lies in delaying hardware procurement. By increasing the effective density of existing clusters, organizations can theoretically serve more models without acquiring additional physical GPUs. However, the operational overhead of introducing a custom scheduler module into production Kubernetes environments is non-trivial. The technology represents a shift from hardware-centric scaling to software-defined optimization, a necessary evolution as the cost of compute continues to outpace budget growth.

Key Takeaways

• **Dynamic Fractionalization:** Nos enables the scheduling of partial GPU pods, partitioning resources in real-time based on demand rather than static integers.
• **Elastic Quotas:** The system allows namespaces to borrow unused resource quotas from others, preventing resource hoarding and improving overall cluster density.
• **Cost Optimization:** By maximizing utilization of existing silicon, Nos aims to reduce the immediate need for additional hardware procurement in high-cost GPU environments.
• **Open Source Alternative:** Following NVIDIA's acquisition of Run:ai, Nos positions itself as an open-source option for granular GPU orchestration.