How to Run Multiple AI Workloads on a Single GPU

Introduction: What is GPU Fractioning?

GPUs are in extremely high demand right now, especially with the rapid growth of AI workloads across industries. Efficient resource utilization is more important than ever, and GPU fractioning is one of the most effective ways to achieve it.

GPU fractioning is the process of dividing a single physical GPU into multiple logical units, allowing several workloads to run concurrently on the same hardware. This maximizes hardware utilization, lowers operational costs, and enables teams to run diverse AI tasks on a single GPU.

In this blog post, we will cover what GPU fractioning is, explore technical approaches like TimeSlicing and Nvidia MIG, discuss why you need GPU fractioning, and explain how Clarifai Compute Orchestration handles all the backend complexity for you. This makes it easy to deploy and scale multiple workloads across any infrastructure.

Now that we have a high-level understanding of what GPU fractioning is and why it matters, let’s dive into why it’s essential in real-world scenarios.

Why GPU Fractioning Is Essential

In many real-world scenarios, AI workloads are lightweight in nature, often requiring only 2-3 GB of VRAM while still benefiting from GPU acceleration. GPU fractioning enables:

• Cost Efficiency: Run multiple tasks on a single GPU, significantly reducing hardware costs.

• Better Utilization: Prevents under-utilization of expensive GPU resources by filling idle cycles with additional workloads.

• Scalability: Easily scale the number of concurrent jobs, with some setups allowing 2 to 8 jobs on a single GPU.

• Flexibility: Supports varied workloads, from inference and model training to data analysis, on one piece of hardware.

These benefits make fractional GPUs particularly attractive for startups and research labs, where maximizing every dollar and every compute cycle is critical. In the next section, we’ll take a closer look at the most common techniques used to implement GPU fractioning in practice.

Deep Dive: Common Techniques for Fractioning GPUs

These are the most widely used, low-level approaches to fractional GPU allocation. While they offer effective control, they often require manual setup, hardware-specific configurations, and careful resource management to prevent conflicts or performance degradation.

1. TimeSlicing

TimeSlicing is a software-level approach that allows multiple workloads to share a single GPU by allocating time-based slices. The GPU is virtually divided into a fixed number of slices, and each workload is assigned a portion based on how many slices it receives.

For example, if a GPU is divided into 20 slices:

• Workload A: Allocated 4 slices → 0.2 GPU

• Workload B: Allocated 10 slices → 0.5 GPU

• Workload C: Allocated 6 slices → 0.3 GPU

This gives each workload a proportional share of compute and memory, but the system does not enforce these limits at the hardware level. The GPU scheduler simply time-shares access among processes based on these allocations.

Important characteristics:

• No actual isolation: All workloads run on the same GPU with no guaranteed separation. On a 24GB GPU, for instance, Workload A should stay under 4.8GB of VRAM, Workload B under 12GB, and Workload C under 7.2GB. If any workload exceeds its expected usage, it can crash others.

• Shared compute with context switching: If one workload is idle, others can temporarily utilize more compute, but this is opportunistic and not enforced.

• High risk of interference: Since enforcement is manual, incorrect memory assumptions can lead to instability.

2. MIG (Multi-Instance GPU)

MIG is a hardware feature available on NVIDIA A100 and H100 GPUs that allows a single GPU to be split into isolated instances. Each MIG instance has dedicated compute cores, memory, and scheduling resources, providing predictable performance and strict isolation.

MIG instances are based on predefined profiles, which determine the amount of memory and compute allocated to each slice. For example, a 40GB A100 GPU can be divided into:

• 4 instances using the 2g.10gb profile, each with around 10GB VRAM

• 7 smaller instances using the 1g.5gb profile, each with about 5GB VRAM

Each profile represents a fixed unit of GPU resources, and workloads can only use one instance at a time. You cannot combine two profiles to give a workload more compute or memory. While MIG offers strict isolation and reliable performance, it lacks the flexibility to share or dynamically shift resources between workloads.

Key traits of MIG:

• Strong isolation: Each workload runs in its own dedicated space, with no risk of crashing or affecting others.

• Fixed configuration: You must choose from a set of predefined instance sizes.

• No dynamic sharing: Unlike TimeSlicing, unused compute or memory in one instance cannot be borrowed by another.

• Limited hardware support: MIG is only available on certain data center-grade GPUs and requires specialized setup.

How Compute Orchestration Simplifies GPU Fractioning

One of the biggest challenges in GPU fractioning is managing the complexity of setting up compute clusters, allocating slices of GPU resources, and dynamically scaling workloads as demand changes. Clarifai’s Compute Orchestration handles all of this for you in the background. You don’t need to manage infrastructure or tune resource settings manually. The platform takes care of everything, so you can focus on building and shipping models.

Rather than relying on static slicing or hardware-level isolation, Clarifai uses intelligent time slicing and custom scheduling at the orchestration layer. Model runner pods are placed across GPU nodes based on their GPU memory requests, ensuring that the total memory usage on a node never exceeds its physical GPU capacity.

Let’s say you have two models deployed on a single NVIDIA L40S GPU. One is a large language model for chat, and the other is a vision model for image tagging. Instead of spinning up separate machines or configuring complex resource boundaries, Clarifai automatically manages GPU memory and compute. If the vision model is idle, more resources are allocated to the language model. When both are active, the system dynamically balances usage to ensure both run smoothly without interference.

This approach brings several advantages:

• Smart scheduling that adapts to workload needs and GPU availability

• Automated resource management that adjusts in real time based on load

• No manual configuration of GPU slices, MIG instances, or clusters

• Efficient GPU utilization without overprovisioning or resource waste

• A consistent and isolated runtime environment for all models

• Developers can focus on applications while Clarifai handles infrastructure

Compute Orchestration abstracts away the infrastructure work required to share GPUs effectively. You get better utilization, smoother scaling, and zero friction moving from prototype to production. If you want to explore further, check out the getting started guide.

Conclusion

In this blog, we went over what GPU fractioning is and how it works using techniques like TimeSlicing and MIG. These methods let you run multiple models on the same GPU by dividing up compute and memory.

We also learned how Clarifai Compute Orchestration handles GPU fractioning at the orchestration layer. You can spin up dedicated compute tailored to your workloads, and Clarifai takes care of scheduling and scaling based on demand.

Ready to get started? Sign up for Compute Orchestration today and join our Discord channel to connect with experts and optimize your AI infrastructure!