Sponsor Spotlight Interview with YellowDog: Powering Quant Teams at Scale

What aspects of your product or approach are most valuable to quant teams — whether that’s speed, transparency, or the ability to handle complex workloads?

“At scale, alpha isn’t limited by models — it’s limited by execution throughput.”

YellowDog is built to deliver sustained throughput at extreme concurrency, which is what actually determines how fast large-scale research finishes. Firms like QRT use YellowDog to run highly parallel simulations and parameter sweeps without hitting scheduler or infrastructure limits.

We scale seamlessly beyond 5 million vCPUs and sustain 40,000+ scheduling transactions per second, even while sourcing capacity dynamically across multiple regions and volatile spot markets. That combination allows us to compress wall time dramatically — often reducing hours or days of computation to a fraction of the time, while also lowering cost.

YellowDog is a drop-in compute platform for quant teams that need more scale, more throughput, and faster paths to alpha.

Visit us at the stand to see real-world simulations and live examples of how we compress wall time for high-scale quantitative workloads.

How do you support scalable research and production workloads across on-prem, cloud, GPU acceleration, and hybrid compute setups?

“One execution layer. Near-100% utilisation. Billion-task workloads in hours, not days.”

YellowDog gives quant teams one execution layer across on-prem, cloud, GPU, vCPU, and hybrid environments.

Workloads are defined once and executed consistently, regardless of where capacity comes from or how the infrastructure is built.

At scale, the difference comes down to throughput and utilisation. YellowDog sustains 40,000+ tasks per second while keeping clusters near 100% saturated, even for short-running, highly parallel workloads. In practice, this means billion-task batches complete in hours rather than days, with lower cost because compute isn’t left idle.

This matters increasingly for FRTB, RWA, and ESG workloads, where firms are running larger, more frequent calculations under tighter time windows. YellowDog ensures these regulatory and risk workloads complete predictably, with time to rerun scenarios, while using the most cost-effective mix of on-prem, cloud, and multi-region spot capacity.

Just as importantly, research and production scale independently. Teams choose the right compute for each stage, while execution behaviour stays consistent from research through to risk and reporting.

With the growing adoption of LLMs, reinforcement learning, and other advanced techniques, where do you see the most immediate and practical impact — and how are you preparing clients to implement them?

“New models change execution patterns — we’re built for the concurrency they demand.”

The most immediate impact of LLMs and reinforcement learning isn’t the models — it’s the execution patterns they introduce.

These workloads drive extreme concurrency, large volumes of short-running tasks, and highly bursty demand for compute. For LLM inference and large-scale simulation, performance depends on sustaining very high task throughput and keeping compute fully utilised — not on scaling individual nodes.

YellowDog is designed specifically for this. Our High-Throughput Scheduling scales with concurrency rather than cluster size, sustaining tens of thousands of tasks per second while maintaining near-100% utilisation, even as demand spikes or capacity is sourced dynamically across regions.

That allows infrastructure teams to support new modelling techniques without over-provisioning or constant tuning. As workloads evolve, YellowDog delivers predictable execution, high utilisation, and stable cost efficiency, ensuring execution infrastructure doesn’t become the limiting factor.

In the age of fragmented infrastructure, how does your technology help connect front-, mid-, and back-office workflows to reduce friction, latency, and reconciliation risk in systematic trading?

“Team autonomy and strict isolation — without making infrastructure the bottleneck.”

In systematic trading, the challenge isn’t connecting teams — it’s giving them full autonomy and privacy without forcing each one to run its own infrastructure.

So for example, our customers require siloed teams to operate in strict isolation, sometimes with complete secrecy, while still accessing the same level of scale and performance. YellowDog makes this possible by providing a shared execution platform with built-in isolation, so teams stay independent without becoming infrastructure owners.

Each team runs workloads in a secure, private execution context, while YellowDog manages scheduling, capacity, and scale underneath. Infrastructure never becomes a dependency for privacy, separation, or speed.

The result is lower operational friction, reduced reconciliation risk, and faster decisions across research, trading, and risk — without compromising confidentiality or control.

Visit us at the booth to discuss this further. YellowDog shows that modern infrastructure can be tuned not just for the firm, but for individual teams, delivering autonomy, privacy, and scale without added complexity or cost.

Don't miss YellowDog at Booth B6 at Future Alpha 2026, March 31 – April 1. Secure your pass today!