Your New Year's Resolution: Cut AI Infrastructure Costs in 2026 With These Five Tools

Insatiable demand for GPU compute resources is currently outstripping global supply, forcing some of even the largest and most highly resourced companies to scale back on their AI initiatives. Shockingly, at the same time, vast amounts of precious AI infrastructure resources go to waste every day, with the majority of GPUs often running underutilized. 

The good news is you can wrangle this problem. Here are the top five approaches for GPU resource management and optimization for 2026:

1. NVIDIA Data Center GPU Manager (DCGM)

NVIDIA DCGM provides a set of metrics and rich telemetry data via Prometheus and integrates with tools like Grafana to provide visibility into a GPU's internal workings. Instead of blindly running workloads, DCGM helps identify inefficiencies, such as pods and namespaces that are gobbling up resources. However, DCGM is an observation solution only; it shows what is going on but does not provide higher-level insights, guidance, or automation to mitigate problems. It also leaves the workload side of the equation untouched, so you can't see the reason for issues that stem from the workload itself.

2. NVIDIA Multiprocess Services (MPS) 

Designed to help improve GPU throughput and efficiency, MPS allows multiple processes to share a GPU. However, this solution does not provide a guarantee that memory is protected, making it unsuitable for multi-tenant environments or situations where security is critical. Furthermore, MPS lacks automation, intelligence, or insights into how or when to best utilize this type of sharing.

3. NVIDIA Multi-Instance GPUs (MIG) 

MIG allows a GPU to be sliced into up to seven isolated, smaller GPU instances, with each instance capable of running a workload. It includes hardware-level security, preventing memory problems and providing hardware isolation. Despite these powerful capabilities, MIG does not provide any automation for the slicing process or intelligence on the right slice sizes for your workloads. It requires considerable manual and ongoing effort to determine the correct slice configuration.

4. Kubernetes Dynamic Resource Allocation (DRA)

This platform-level solution, primarily for Kubernetes, allows for the allocation of resources beyond CPU and memory using an abstraction and plug-in framework. However, DRA is a tool that requires you to manually build on top of its capabilities, and it requires specific drivers that are not seamlessly supported in all environments.

5. Pepperdata Dynamic Resource Optimization for GPUs

All the solutions above share common limitations: they lack an intelligence layer and automation, and they fail to provide insight into the workload to potentially change it or assign it based on the platform. 

Pepperdata's dynamic resource optimization solution for GPUs bridges the gap left by these existing tools to automatically eliminate waste resulting from underutilized GPUs.

Pepperdata for GPUs automatically and dynamically:

• Partitions GPUs into slice pools
• Analyzes workloads to determine the appropriate GPU resources required 
• Adjusts GPU slice pool capacity based on real-time usage

Here's how Pepperdata works, step by step:

1. Observe and Fingerprint:

   Pepperdata examines running workloads and determines, for example, that a job might only require one half or one third of a GPU. Pepperdata then assigns each workload a fingerprint that captures the ID of the workload and the target GPU slice.
   
   

2. Slice and Pool:

   Based on the workload findings, Pepperdata automatically configures GPU slice pools (e.g., pools of full, half, or third slices).
   
   

3. Assign Workloads:

   Based on the fingerprint, Pepperdata automatically and continuously updates applications to request the GPU slices appropriate for their size.
   
   

4. Update:

   Since application demands and GPU requirements are dynamic, Pepperdata continuously updates the workload fingerprints and dynamically adjusts the GPU slice pools accordingly.

Figure 1: Pepperdata automatically addresses the industry-wide problem of costly, underutilized GPU resources. In this example, workloads that previously required nine full GPUs now only need 5.5 GPUs to run, resulting in 38% savings. 

The result is that fewer GPUs are needed, leading to significant savings in the cloud by or on-premise by enabling more work to be done on the same hardware, without requiring manual workload tweaks, manual GPU re-slicing, or constant monitoring.

Why not make it your New Year's resolution to get a handle on runaway AI infrastructure waste and cost in 2026? To learn more, download your free white paper that delves into all of this in much more detail or check out our latest video: Stop Wasting Precious GPUs: Dynamic Resource Optimization with Pepperdata.