Improving GPU Utilization: Strategies and Best Practices

GPU infrastructure represents one of the largest capital investments in modern infrastructure, yet research shows that most organizations achieve less than 30% GPU utilization across their machine learning workloads. With individual H100 GPUs costing upwards of $30,000 and cloud instances running hundreds of dollars per hour, this underutilization translates to millions in wasted compute resources annually. 

The pressure to optimize has never been more intense. As AI workloads scale exponentially and model complexity grows, engineering teams face the dual challenge of maximizing performance while controlling infrastructure costs. 

Let's explore how GPU utilization works, why it matters, and how to drive better performance from your AI infrastructure.

Key highlights:

• Low GPU utilization costs enterprises millions annually in wasted compute resources and delayed model deployments

• Strategic optimization can increase GPU memory utilization by 2-3x through proper data loading, batch sizing, and workload orchestration

• Monitoring tools and KPIs help track compute utilization, memory bandwidth, and identify bottlenecks before they impact production

• Modern AI platforms with GPU-aware scheduling can automatically optimize resource allocation based on workload patterns

What Is GPU Utilization?

GPU utilization measures the percentage of time a graphics processing unit actively performs computational work versus sitting idle during a given period. This metric encompasses multiple dimensions, including compute utilization (how busy the cores are), memory utilization (how much memory is being used), and memory bandwidth utilization (how efficiently data moves between memory and cores). 

Unlike CPU utilization, which often focuses on a single metric, GPU utilization requires monitoring these multiple components simultaneously since bottlenecks in any area can leave expensive compute resources underutilized.

The complexity stems from the GPU's parallel architecture. While it might show 100% memory usage, its compute cores could be idle waiting for data, resulting in poor overall utilization despite appearing "full" by one metric.

Why GPU Usage Matters for AI Workloads

GPU and memory bandwidth usage directly impacts the economics and performance of AI operations at scale. Every percentage point of improved utilization translates to measurable business value across multiple dimensions.

• Reduce Cloud Costs and Idle Time: Organizations typically waste 60-70% of their GPU budget on idle resources, with proper utilization strategies cutting cloud GPU costs by up to 40% through better resource scheduling and workload distribution.

• Improve AI Training Throughput: High GPU usage accelerates model training cycles, enabling teams to iterate faster, test more hypotheses, and reduce time-to-market for AI features from months to weeks.

• Maximize ROI on Infrastructure Spend: With each GPU representing $30,000+ in capital costs, improving utilization from 30% to 80% effectively doubles infrastructure capacity without additional hardware investment.

• Support Sustainability Initiatives: Data centers consume 1-2% of global electricity, and optimized GPU usage reduces carbon footprint by minimizing the number of GPUs needed for equivalent computational output.

The Cost of Low GPU Utilization

The hidden costs of underutilized GPUs extend far beyond the obvious infrastructure waste, creating cascading inefficiencies throughout the AI development lifecycle.

Common Challenges That Lead to Low GPU Usage

Understanding why GPUs aren’t being fully utilized is critical if you want to fix performance issues without just throwing more hardware at the problem. Below are some of the biggest culprits teams run into, along with what typically drives them.

Slow Data Loading

One of the most common reasons GPUs sit idle is that the data pipeline can’t keep up. When data transfer from storage lags, the GPU ends up waiting around instead of crunching numbers. Key bottlenecks to watch for include:

• Network latency between storage and compute nodes

• Insufficient data preprocessing capacity

• Lack of data prefetching or caching mechanisms

CPU Bottlenecks

Even if the GPU is ready to go, a slow or overloaded CPU can throttle the entire pipeline. This often happens when preprocessing or data augmentation tasks can’t keep pace, creating delays that starve the GPU of work. Typical problem areas include:

• Single-threaded data transformation code

• Inadequate CPU-to-GPU ratio in cluster design

• Python GIL limitations in data loading

Inefficient Memory Access

Sometimes the GPU looks busy on paper, but performance still lags. This usually points to memory access issues, the GPU cores spend more time waiting for data than actually processing it. Some common causes are:

• Non-coalesced memory reads

• Excessive memory transfers between the host and the device

• Suboptimal tensor layouts for GPU architecture

Poor Parallelization

GPUs shine when workloads can be massively parallelized, but not every setup takes advantage of that. If your code or algorithms aren’t distributing work properly, you’ll see low utilization no matter how powerful the hardware is. Look for these warning signs:

• Small batch sizes that underutilize GPU cores

• Sequential operations that can't be parallelized

• Improper work distribution across streaming multiprocessors

Compute-Insensitive Workloads

Finally, not every workload is a good fit for GPUs. If the task itself doesn’t require heavy parallel compute, forcing it onto a GPU won’t magically improve performance. Examples where this happens include:

• Simple linear models or decision trees

• I/O-bound data preprocessing tasks

• Small-scale inference with minimal parallelism

Key Benefits of Optimizing GPU Memory Utilization

Strategic GPU optimization delivers compound benefits that extend beyond simple cost savings to fundamentally transform AI development velocity and scale.

• Higher Throughput for AI Models: Optimized GPU memory utilization enables processing larger batches and more complex models simultaneously, increasing training throughput by 2-3x without hardware changes. Teams can complete more experiments daily, accelerating the path from research to production deployment.

• Fewer GPUs Required Per Job: Efficient memory usage allows consolidating workloads onto fewer GPUs, reducing both capital expenditure and operational complexity.

• Lower Training Time and Cost: Maximizing GPU utilization cuts training time from weeks to days for large models, directly reducing compute costs. Every 10% improvement in utilization typically yields 15-20% cost savings due to reduced runtime.

• Better Resource Efficiency and Allocation: Optimized environments enable dynamic resource sharing between teams and projects, improving overall cluster efficiency. Smart scheduling ensures GPUs stay busy with queued jobs rather than sitting idle.

How to Increase GPU Utilization: Strategic Approaches

Boosting GPU performance isn’t just about cranking up the hardware — it’s about smart system design and matching the right workloads to the right infrastructure. The strategies below focus on eliminating bottlenecks and making sure every GPU cycle delivers value.

Co-Locate Compute and Storage

Data needs to get to the GPU fast. When storage sits too far from compute, you introduce network bottlenecks that drag utilization down. By bringing compute and storage closer together, you can dramatically improve throughput in data-heavy workloads. Best practices include:

• Deploy NVMe storage directly on GPU nodes

• Use high-speed interconnects like InfiniBand

• Implement distributed caching layers

• Minimize data movement across availability zones

Schedule Jobs Based on Load Patterns

If GPUs are constantly overbooked or sitting idle, that usually points to scheduling inefficiencies. Smarter scheduling ensures workloads align with available resources, reducing fragmentation and wasted cycles. Key approaches are:

• Profile workloads to understand resource needs

• Implement gang scheduling for distributed training

• Use predictive scheduling based on historical patterns

• Enable job preemption and checkpointing

Use GPU-Specific Orchestration Tools

Generic orchestration often misses GPU-specific nuances, leaving performance on the table. Platforms designed with GPU workloads in mind help maximize efficiency by managing placement, scaling, and sharing intelligently. Strong options include:

• Deploy Kubernetes with GPU device plugins

• Implement NVIDIA GPU operators for resource management

• Use ML-specific schedulers like Kubeflow

• Enable GPU sharing for smaller workloads

Select Models Suited for GPU Execution

Not all models play nicely with GPU architectures. Picking the right models for the hardware ensures your GPUs run at full throttle rather than stalling on inefficient computations. Model selection guidelines include:

• Prioritize models with high arithmetic intensity

• Use transformer architectures for parallelization

• Avoid models with irregular memory access

• Consider model size relative to GPU memory

Scale Resources with Demand Forecasting

Overprovisioning leads to waste, while underprovisioning creates bottlenecks. By forecasting demand and scaling accordingly, you keep utilization steady while avoiding both idle GPUs and job starvation. Proven tactics are:

• Analyze historical usage patterns

• Implement autoscaling based on queue depth

• Use spot instances for batch workloads

• Reserve capacity for critical training jobs

Monitor GPU Utilization: Best Practices for Enterprises

Keeping GPUs running at peak efficiency is not just about setup. It is an ongoing process. Enterprises need structured monitoring practices that balance performance gains with implementation complexity. Below are five proven steps to monitor and optimize GPU usage.

1. Tune Batch Sizes

Batch size is one of the simplest but most impactful levers for improving GPU utilization. The goal is to fully load GPU memory without breaking training stability or convergence. Smart batch tuning can improve utilization by 20–30% compared to defaults. Best practices include:

• Start with the largest batch that fits in memory

• Use gradient accumulation for effective larger batches

• Profile memory usage during training

• Adjust based on model convergence metrics

• Consider mixed batch sizes for heterogeneous workloads

2. Implement Mixed Precision Training

Modern GPUs perform best when mixed precision is used. This combines FP16 and FP32 calculations to speed up training and reduce memory load. When implemented correctly, you can train with bigger batches, run faster, and maintain accuracy. Key tactics include:

• Enable automatic mixed precision in frameworks

• Use tensor cores on modern GPUs

• Use the GPU usage monitor to track numerical instabilities

• Implement loss scaling for gradient flow

• Validate model accuracy with full precision

3. Use Distributed Training

For large models or datasets, a single GPU is often not enough. Distributing workloads across multiple GPUs increases utilization across the cluster and shortens training cycles. To maximize efficiency:

• Implement data parallelism for large datasets

• Use model parallelism for memory-constrained models

• Optimize communication patterns between GPUs

• Balance the compute and communication overhead

• Profile scaling efficiency regularly

4. Preload and Cache Data

Even the fastest GPU will stall if it is waiting for input. Efficient data handling ensures the pipeline continues to feed GPUs without interruption. Recommended practices include:

• Implement asynchronous data loading pipelines

• Cache frequently accessed datasets in GPU memory

• Use memory-mapped files for large datasets

• Prefetch the next batch during current computation

• Optimize data formats for GPU consumption

5. Prioritize Compute-Bound Operations

Not every operation benefits from running on a GPU. Shifting the right workloads to GPUs and optimizing them for parallelism ensures you get maximum value. Focus areas include:

• Move preprocessing to CPU or dedicated hardware

• Batch operations to amortize kernel launch overhead

• Fuse operations to reduce memory bandwidth pressure

• Use optimized libraries like cuDNN and cuBLAS

• Profile kernels to identify optimization opportunities

Top KPIs to Measure GPU Usage Effectively

Comprehensive monitoring requires tracking multiple metrics that together provide a complete picture of GPU utilization and identify optimization opportunities.

• Compute Utilization: Measures the percentage of time GPU cores actively execute instructions, with targets typically above 80% for training workloads and 60% for inference.

• Memory Utilization: Track GPU usage that is both allocated and actively used, helping identify opportunities for larger batch sizes or model parallelism when underutilized.

• Memory Bandwidth Utilization: Monitors data transfer rates between GPU memory and cores, revealing bottlenecks in memory-bound operations that limit overall performance.

• Use of Tools: Deploy comprehensive monitoring solutions like NVIDIA's GPU monitoring tools, including nvidia-smi, DCGM, and Nsight Systems for deep performance analysis and bottleneck identification.

• Autoscaling Logs and Model Dashboards: Implement real-time dashboards showing cluster-wide GPU utilization trends, job queue depths, and automatic scaling events to optimize resource allocation dynamically.

Optimize GPU Usage with Mirantis

The path to optimal GPU utilization requires more than just monitoring tools—it demands a comprehensive platform that understands the unique requirements of AI infrastructure solutions. Mirantis provides enterprise-grade orchestration and management capabilities specifically designed for GPU-intensive workloads.

Our Mirantis k0rdent AI platform delivers automated GPU optimization through: 

• Intelligent workload scheduling

• Dynamic resource allocation

• Built-in best practices for AI inferencing platform deployments 

With native support for virtualization and multi-tenancy, organizations can maximize their GPU investments while maintaining isolation between teams and projects. The platform's advanced monitoring capabilities provide real-time visibility into utilization across your entire infrastructure, automatically identifying bottlenecks and suggesting optimizations. Whether you're running distributed training jobs or scaling AI inference workloads, Mirantis ensures your GPUs operate at peak efficiency.

Book a demo today and see how Mirantis helps enterprises improve GPU utilization and drive greater efficiency across AI workloads.