Executive Summary

In the era of AI-driven enterprises, GPUs have become the most critical—and expensive—computational resource in the data center. However, despite their power, GPUs are often underutilized. Many AI workloads, especially in inference, model tuning, and early-stage development, do not need an entire GPU. Yet traditional allocation methods assign an entire GPU per job, leading to wasted compute, inflated costs, and reduced cluster throughput.

GPU fractionalization solves this inefficiency by enabling multiple AI jobs to share a single physical GPU, allocating only the resources each job actually needs. This dramatically increases GPU utilization, allows more workloads to be scheduled in parallel, and significantly reduces the per-job cost of GPU usage.

This use case explores how fractionalization works, what types of workloads benefit most, and how enterprises can boost throughput and reduce GPU costs by 2× or more.

Problem

Modern AI workloads vary widely in their GPU resource requirements:

• Lightweight inference jobs may use only 5–15% of a GPU
• Early-phase model development or parameter tuning may use 20–30%
• Even production batch pipelines often sit idle between data transfers

Yet conventional GPU orchestration assigns jobs one GPU at a time, regardless of need. The result:

• Low GPU utilization (often 20–30% average across a cluster)
• High costs for short or low-power job
• Resource contention that slows scheduling of new jobs

Example

A team runs 10 concurrent inference pipelines. Each uses only 10% of GPU memory and compute, but each occupies a full A100 GPU. As a result, 10 jobs consume 10 GPUs—but only 1 GPU’s worth of capacity is actually used

Solution: GPU Fractionalization for Efficient Sharing

GPU fractionalization allows a physical GPU to be partitioned into multiple logical GPU slices, each assigned to a different AI job. These slices can operate independently, each with isolated:

• GPU memory
• Compute cores (CUDA cores, Tensor cores)
• Scheduling contexts

This enables multiple workloads to safely and efficiently share a single GPU.

There are several implementation approaches

• MIG (Multi-Instance GPU) by NVIDIA: Supported on A100 and H100, splits the GPU into up to 7 isolated slices
• Software-based fractionalization using Kubernetes device plugins or resource schedulers
• Time-slicing for workloads tolerant to context switching

Benefits of GPU Sharing via Fractionalization

Quantified Impact

Let’s say a company operates a 20-GPU cluster of NVIDIA A100s for inference and training. Typical workloads:

• 70% are light inference jobs using <15% of GPU
• 30% are moderate development jobs using 30–50%

Without fractionalization

• Each job takes a full GPU
• Only ~25% of each GPU is used
• Cluster is maxed out with ~20 jobs at once

With fractionalization

• Inference jobs are packed 4–7 per GPU
• Development jobs share GPUs with light workloads
• Effective concurrent job count increases to 50–70
• Utilization jumps to 80%+

Cost Efficiency

• A100 spot instance cost: ~$2.50/hr
• 10 lightweight jobs on 10 GPUs = $25/hr
• Same jobs on 2 GPUs with fractionalization = $5/hr
• Savings: $20/hr, or ~$175,000/year for a mid-size team

Application Scenarios

1. High-Volume Inference

Companies running thousands of inference requests per second (e.g., chatbots, recommendation engines, real-time analytics) can:

• Consolidate requests across shared GPUs
• Assign fractional slices to microservices
• Isolate workloads while reducing idle time

Impact

Reduces GPU footprint by 60–80% with no SLA degradation.

2. Interactive Model Development

Data scientists running notebooks and training experiments often underuse GPU resources. Fractionalization enables:

• Multiple notebooks to share a single GPU
• Faster experiment cycles (no waiting for GPU assignment)
• Better ROI on limited GPU inventory

Impact

Doubles developer productivity and reduces GPU starvation.

3. Hyperparameter Optimization

Tuning models with small batch sizes and fast iteration times leads to short-lived, low-utilization jobs. With fractionalization:

• Dozens of tuning jobs run in parallel
• Resources are fully consumed, not blocked
• Jobs complete faster without scaling GPU count

Impact

Completes HPO runs 2–4× faster at 50% of the cost.

Technical Enablers

• MIG Support: Native to NVIDIA A100/H100 via CUDA APIs
• Kubernetes Plugins: Device plugins that expose GPU slices as schedulable resources
• Schedulers (e.g., Volcano, KubeSlice): Handle fair sharing and resource guarantees
• Runtime Isolation: Ensures security and stability for mixed tenant jobs
• Transparent Memory Isolation: Prevents leaks and overconsumption

Future Outlook

As AI workloads diversify and GPU demand intensifies, fractionalization will become a default strategy for cost-effective scaling. Enterprises will:

• Design job scheduling policies that default to shared GPUs
• Use fractionalization-aware autoscalers to right-size clusters
• Integrate usage-based billing to charge per fraction consumed

Ultimately, GPU fractionalization will be as fundamental as multi-core CPU sharing—unlocking massive scale without massive cost.

Conclusion

GPU fractionalization enables enterprises to get more out of every GPU—more jobs, more throughput, and more savings. By dynamically partitioning GPU resources to fit the size of each job, organizations can:

• Boost utilization from 30% to 90%
• Cut costs by up to 70%

Accelerate AI delivery without expanding infrastructure
In a world where every GPU minute matters, fractionalization is the key to affordable, scalable AI.