Strong Compute - Your High-Performance Training Infra Platform 🔋

CV Deep Dive

Today, we’re talking with Ben Sand, Founder and CEO of Strong Compute.

Making Ops the Heroes.
Strong Compute is a high-performance training infrastructure platform - designed to help AI teams go from prototype to production without getting buried under ops complexity. Built for researchers, engineers, and infra teams working with tens to thousands of GPUs, Strong Compute handles the full orchestration pipeline—from workload scheduling and data movement to resource provisioning and monitoring and robust multicloud cost controls—without requiring teams to reinvent cluster infrastructure from scratch. The goal of Strong Compute is to make Ops the Heroes in the model development process.

Founded by Ben, a hardware builder and longtime AI infrastructure operator, Strong Compute was born out of the realization that most AI orgs—even well-funded ones—struggle to train efficiently at scale. Poor GPU utilization, storage bottlenecks, opaque queuing systems, and organizational gridlock mean that compute often sits under-utilized, while jobs stall in queues or fail silently. Strong Compute helps teams spin up clusters in under an hour, manage multi-cloud deployments, and improve throughput and cost-efficiency without adding headcount. 

Today, Strong Compute is being used by teams across research labs, vision startups, and enterprise AI groups—particularly those training on modest budgets but requiring high reliability. In this conversation, Ben explains why most infra stacks are wildly inefficient, and why better training infrastructure—not just better models—will be the differentiator in AI's next phase.

Let’s dive in ⚡️

Read time: 8 mins

Our Chat with Ben 💬

Ben, welcome to Cerebral Valley! First off, introduce yourself and give us a bit of background on you and Strong Compute. What led you to start Strong Compute?

Hey there! I’m Ben, CEO of Strong Compute. A bit about my background—I’ve built a lot of computers. Probably more than a thousand computers by hand! I’ve also worked in and invested in AI companies for the past 15 years or so. About 15 years ago, I was running a program called Brainworth that taught kids how to build neural networks—so AI education has been a long-running thread for me.

Over time, we explored different areas of AI, especially around training, and eventually realized that infrastructure for AI training just doesn’t work the same way it does for the web. With web infrastructure, you have cohesive platforms across all the clouds, things behave predictably, and it all mostly works. But when it comes to training AI models, it’s not that smooth. We were helping people train models and kept running into the same roadblocks, and that’s what we wanted to solve.

How would you describe Strong Compute to the uninitiated developer or AI team? Who is finding the most value in what you’re building? 

Between getting your data and having something ready for inference, there’s Strong Compute. What we want to do is make it so that the ops people become the heroes of that journey—from raw data to inference—instead of being the overlooked or underappreciated part of the process, which is often the case today.

Our ICP is people building models who are working with tens, hundreds, or even thousands of GPUs.

Training is normally associated with large companies and billions of dollars of compute power. How are you challenging this assumption internally at Strong Compute?

Certainly, larger companies have the scale to handle that kind of training. But there are also a lot of smaller companies doing meaningful model training. I think when most people hear “model building,” they immediately think of something like ChatGPT and assume they’ll need billions of dollars. Before language models took over the narrative, plenty of teams were training models on vision-related tasks. Outside of self-driving, a lot of them were able to get great results with relatively modest budgets.

We’ve seen companies valued in the hundreds of millions to billions that built their AI infrastructure with something like 64 GPUs. They were doing vision-centric work—exactly the kind of teams we support at Strong Compute. So, if you’re trying to build a state-of-the-art language model or solve self-driving, you’re going to need serious capital for your AI infra stack. If you’re working on almost anything else, you can absolutely get results on a startup-scale budget.

Walk us through Strong Compute’s platform. What use-case should people experiment with first, and how easy is it for them to get started? 

If you’re building models, chances are you’ve done some work on a single GPU or node and feel comfortable there. But once you try to scale up to a cluster, things get hard—fast. It also gets expensive very quickly. One of the things we’ve proven through our hackathons is that you can go from a single GPU to a full cluster in under an hour. That’s a pretty smooth onboarding compared to the months or even years it might take to piece it all together on your own.

Normally, that journey involves stitching together a bunch of tools—one for orchestration, another for provisioning, then storage, financial controls, and so on. And if you’re going multi-cloud, you’ve got to repeat that for every provider. It’s a huge lift.

At Strong Compute, we’ve brought all of that into one place. You don’t need to hunt down separate providers. If you know how to do deep learning—if you can run PyTorch on a single machine—and you just want a bigger computer, we make it easy to scale that into a full cluster.

What we see with a lot of startups is they assume they need to buy GPUs, and they try to get them as cheaply as possible. So they burn through their accelerator credits, hop on something like GPU List, find the cheapest machines they can, and dive in. As they scale, it gets way more complex than expected. These setups break more often than people realize, and suddenly you’re duct-taping everything together just to get something that kind of works.

If they’re really good—and persistent—they might get to the point where they can reliably train on 100 or 200 GPUs. But it often takes a couple of years of grinding to get there.

How are you measuring the impact that Strong Compute has on your customers’ AI infra? Are you optimizing for speed, cost or accuracy, or all three?

All of the above, really—and that’s exactly why we wanted to do this deep dive with you. We want to open up a conversation with ops teams to figure out which of these pain points is actually the most painful, because we’ve built solutions for all of them. It’s easier to set up, it’s a lot cheaper, and it helps you get your product to market faster. But depending on the team, those priorities are going to vary. 

Over the last year, we’ve had a great run with developers—more than 600 engineers have come onto the platform and trained models. Now we want to go deeper with the ops folks. That’s who we are at our core. And honestly, every engineer who comes to our events has stories—story after story—about things going sideways on the ops side inside their org.

We want to change that. We want the ops people to be the heroes of the story.

How would you describe the difference between the needs of Engineers and DevOps people, in the context of Strong Compute? How are you thinking about serving them differently? 

People often assume, “Didn’t DevOps already solve this?” We had platform teams and engineering teams—surely those came together years ago. And that’s true for traditional software, cloud infrastructure, and web apps. But in AI, that convergence hasn’t happened. At least, not universally.

One of our goals at Strong Compute is to give developers freedom—to let them access hundreds of GPUs whenever they need, and to use them like it’s their own machine. But when we talk to ops teams, the response is often very different. They'll say, “If you want to install something with apt, you need to file a ticket.” And it’s hard not to ask: how does anyone get anything done in that setup?

We’ve seen this friction play out even in major research institutions with massive GPU clusters. Developers will come to us and ask, “How can I train a model using just a quarter of a GPU?” And our first reaction is, “Why would you need to do that? Is this a side project or something?” But it’s not. That’s all they’re allowed to use. They’ve got to ask a supervisor, who then has to ask a manager, and so on. It’s multiple layers of approvals just to access resources sitting idle. So what do they do? They try to code their way out of the bureaucracy—because it’s the only part of the system they control.

The most extreme case we’ve heard recently involved an organization with a few thousand H100s. Their internal platform was unstable—jobs would fail or disappear entirely—so the ops team was already under fire. To keep things efficient, they started pushing users off idle nodes. So what did the developers do? They started running fake jobs—dummy workloads—just to hold onto their machines. You end up with a $100 million infrastructure cluster running tasks that do nothing, just so developers can avoid losing access. Why? Because the teams aren’t playing on the same side.

This isn’t isolated. In many organizations, once GPUs are allocated, people hoard them—even when they’re not using them—because they worry they won’t get them back. It becomes this “use it or lose it” mindset, like end-of-year government budget cycles. We spoke to one organization where internal groups had gone so far out of their way to block GPU access from others that it took them six months just to figure out how many GPUs they had leased—with Strong Compute you can have that data instantly, in a format managers can digest easily.

This isn’t a technology problem—it’s an organizational one. And unless the culture around ops and infra changes, even the best hardware won’t be used to its full potential.

How do you perceive the difference in AI adoption between Silicon Valley and some of the non-tech industries you service, like construction and healthcare? 

I haven’t met any developers in AI who are genuinely happy with how their clusters run. Sure, people are getting work done, and these GPUs are being bought and deployed—they’re not all sitting idle. But a shocking amount of them are doing nothing.

If people really dug in and looked at how these resources are actually being used, it would scare them. There’s this assumption that you have to spend an enormous amount of money on GPUs to get meaningful results. And while that’s true for some problems, for many others it’s simply a reflection of how inefficiently those GPUs are being used.

Before we even got into orchestration, one of the first things we focused on was accelerating training times. We were able to take a training job that previously took 24 hours and reduce it to just 5 minutes—producing the exact same results. We did it using 30 times fewer GPU hours overall. We used more GPUs in parallel, but for a much shorter duration. The result? A job that was not only a thousand times faster, but also 30x cheaper in terms of total compute cost.

Why? Because most of these systems don’t communicate well. GPUs need to be constantly fed with data, but storage systems often aren’t built to support that properly. And the workload schedulers aren’t optimized to ensure the right data is in the right tier of storage at the right time. Unless you're at a massive AI organization with a dedicated infra team that’s obsessing over these details, the inefficiencies stack up fast.

When you zoom in on what’s actually happening, the reality is that GPUs are often only active about 1% of the time. The rest of the time, they’re sitting idle—waiting for data to move, blocked by storage constraints, or simply dark and unused. Meanwhile, you’ve got jobs sitting in queues elsewhere in the org, waiting for compute. And when those jobs do run, the GPUs are still mostly underutilized. It’s an incredibly wasteful cycle that most people never see.

2025 is said to be the year of the Agent. How does this factor into your product vision (or internal processes) for Strong Compute? 

We use a lot of AI tools. The ones I tend to prefer are those that decouple things in the right way—they make it really easy to swap out models while staying in the same environment.

I’m thinking in particular of tools for coding, like RooCode. I can build a workflow around how I’m using an agent, but I also have the flexibility to switch out the underlying model based on whatever is best at the moment.

That’s likely where everything is heading. There are so many models out there, and you want the one that’s best suited for the task—whether that means fastest, cheapest, or highest quality. It really depends on the job, and having that flexibility is key.

2025 has already been a whirlwind of AI releases - especially with DeepSeek in Q1. How did you perceive that moment and the efficiency gains it would unlock? 

I wouldn’t bet against DeepSeek. The efficiency gains are very real—but what’s even more important is what those gains unlock. It pushes model training past a threshold that suddenly makes it viable for a lot more organizations to build their own purpose-built models for specific tasks. So instead of relying on a general-purpose model like ChatGPT that helps everyone but maybe doesn’t know you very well, you can now have your own personal agent that costs about the same to run—but is trained specifically for your needs.

Before DeepSeek, training a high-quality, purpose-built agent was prohibitively expensive for most applications. Now, I think we’re at the point where, for almost any role in a reasonably sized organization—or even for a single high-value customer—you can train or fine-tune a dedicated model.

For us, fine-tuning and training are essentially the same thing. I know there’s a lot of discussion around whether something counts as fine-tuning versus full training, but from a compute standpoint, it’s the same. There are architectural differences, sure, but in terms of cost and effort, they’re closely aligned.

We’re clearly on a trajectory where personalized models will become the norm across a wide range of use cases. I’ve even seen well-fine-tuned 13B models outperform state-of-the-art, larger models in specific coding tasks—areas where you’d expect the giant general models to dominate. But that’s just not always the case. More and more, we’re going to see smaller, fine-tuned models emerge as the best tools for very specific jobs.

Lastly, tell us about the team at Strong Compute. What makes you special, are you hiring, and what do you look for in prospective team members joining the team? 

We’re based in Australia and San Francisco. What I really love about our team is how deep they go across the stack. We’ve got people who are incredibly comfortable with hardware, others who are strong on models, and others who really understand infrastructure and control systems. And in many cases, it’s the same person covering multiple of those areas.

That kind of full-stack fluency is something I don’t see as often in other teams. There’s a level of depth and versatility here—people who truly understand the entire pipeline end to end—and that’s been a big differentiator for us.

Conclusion

Stay up to date on the latest with Strong Compute, learn more about them here.

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