GPU Cost Optimization: How to Reduce Costs with GPU Sharing and Automation

Over the past year, I’ve been hearing the same story from customer after customer: GPU costs are spiraling out of control.

A couple of years ago, GPUs inside a Kubernetes cluster were mostly the domain of AI-focused companies. Not anymore. Today, everyone is running GPU workloads, whether for machine learning, analytics, or even just testing and development.

And that’s where the trouble starts.

The $5,000/month developer habit

Take the NVIDIA H100, for example. On AWS, a single H100 instance can cost around $5,000 per month.

Here’s a pattern we see all the time:

• A developer spins up a GPU for a job
• Runs a quick experiment
• Leaves it running while coding or heading to lunch
• Comes back hours later for the next test
  

The result? That GPU sits idle most of the day, silently burning through thousands of dollars while doing almost nothing.

Even during a test, most workloads don’t fully saturate the GPU. They often end up using only a fraction of its capacity for a few minutes before reverting to idle. That’s money literally disappearing into thin air.

How to fix it: GPU time-slicing and Multi-Instance GPU (MIG)

The good news: you don’t need to buy more GPUs or beg developers to “remember to shut things off. There are two proven techniques to reduce waste: GPU Time-Slicing and Multi-Instance GPU (MIG).

Let’s take a closer look at these GPU sharing methods.

GPU time-slicing

Think of this like CPU multitasking. With time-slicing, multiple workloads can share the same GPU, each getting time on the hardware without needing the full throughput. This approach is perfect for development or lightweight jobs.

Multi-Instance GPU (MIG)

This method is even more powerful. MIG lets you split a single GPU into multiple isolated “micro-GPUs.” Each slice behaves like a fully independent GPU, with its own clock speed and memory bandwidth.

That means you can take a $5k/month H100 and let eight developers share it – all working in parallel, all at full speed, without stepping on each other’s toes.

The cost difference is massive compared to running eight separate GPUs or forcing devs to wait for hardware.

How Cast AI automates GPU sharing

At Cast AI, we’ve built GPU time-slicing and MIG directly into our automated Kubernetes management platform. Our automated platform streamlines the deployment and management of these complex GPU methods, integrating seamlessly with your autoscaler to ensure optimal resource allocation.

The impact is stunning:

• Customers are saving far more than with CPU tuning alone.
• Even a small improvement in GPU utilization translates into significant dollar savings, as GPUs are exponentially more expensive.
  

Moreover, Cast eliminates the need to choose between cost efficiency and performance isolation. Our platform enables teams to run more GPU workloads with fewer resources, resulting in considerable cost savings without sacrificing the performance or reliability that your essential applications require.

Cast AI includes features that keep the gap between utilization and requested resources very tight, by default and automatically.

Example: GPU provisioned is the same as GPU requested, yet utilization is poor

As you can see in the graph, thanks to Cast’s GPU sharing the provisioned GPUs are now lower than requested GPUs:

The shared GPUs lead to a big dent in daily compute costs without compromising performance:

The setup takes a few minutes via node template configuration:

Bonus: Spot Instance automation

By time-slicing GPUs, four developers can share a single H100, reducing the cost per developer by 75%. When Spot GPUs are available, leveraging them can further cut costs by up to 60%. 

Combined, Cast AI can reduce GPU-related expenses for development by as much as 93% per developer, thanks to the synergy of time slicing and Spot Instance optimization.

Check out this blog post for more details: GPU Sharing in Kubernetes: How to Cut Costs and Boost GPU Utilization with Cast AI

Wrap Up

GPUs have become the new budget killer in the cloud. But, with the right automation and resource-sharing methods, such as time-slicing and MIG, you can significantly reduce costs while accelerating development.