Summary
STAC New York 2026 revealed that quant firms are moving AI into production by prioritizing GPU efficiency, multimodal data pipelines, and unified high-performance data infrastructure.
Running modern Part of our STAC quant blog series
STAC events are known for deep technical benchmarking, but this year’s STAC Summit New York 2026 conversations moved beyond isolated speed metrics to focus on performance, data complexity, and operating economics. Across the event, one theme became clear: Firms are shifting from AI experimentation to AI production.
What stood out
The most striking takeaway from the event was a clear shift toward pragmatism. A year ago, many AI conversations in quant finance were exploratory or speculative. Today, the tone is intensely commercial.
Budgets are under pressure, forcing teams to prioritize initiatives that deliver measurable business impact over innovation for innovation’s sake. Traditional benchmarking topics naturally diverged into infrastructure efficiency. The market is thinking more holistically. Infrastructure decisions are no longer just an IT concern. They now shape trading outcomes, innovation velocity, and operating economics.
The two themes that dominated the conversation at STAC
1. AI infrastructure efficiency: Beyond the GPU scramble
While demand for GPUs remains intense, the industry conversation has matured from chip acquisition to raw utilization efficiency. High-cost GPU clusters cannot sit idle; they must be continuously fed with data. Today, firms are focused on eliminating architectural bottlenecks that stall training loops or inference pipelines and drive up capital spend. Furthermore, as the market shifts from training large models to deploying them at scale, optimizing key-value (KV) caching has become a top priority to minimize latency and maximize hardware throughput.
2. Market data complexity: Scaling without silos
Outside of AI, firms are grappling with relentless market data growth and the engineering challenge of maintaining deterministic performance during extreme burst conditions. The core hurdle is operational complexity: Firms need to scale their research and trading infrastructure seamlessly. The goal now is to handle skyrocketing data volumes without creating fragmented data silos that trap insights and slow down trading teams.
Where the market is heading: The multimodal paradigm shift
The new alpha frontier: Multimodal data fusion
To generate differentiated alpha in increasingly crowded markets, firms are rapidly expanding what constitutes a trading signal. The future of quantitative strategies no longer relies solely on traditional, structured time-series data. Instead, the edge belongs to firms that can seamlessly combine tick data with unstructured alternative sources, such as global news feeds, corporate research transcripts, regulatory filings, and voice data, in real time.
This shift toward multimodal AI pipelines allows alpha generation engines to correlate market-moving sentiment with order book dynamics simultaneously. However, ingesting, normalizing, and processing these disparate data types has exposed major structural gaps in legacy infrastructure.
The infrastructure bottleneck: Legacy tech at the breaking point
For most firms, the urgent question is no longer, “What models should we use?” but rather, “Can our platform support this data scale and variety?” Quant strategies are only as good as the pipelines supporting them. Legacy architectures were designed for a different era. Fragmented data silos and separate systems for market data and unstructured text are struggling to support multimodal workloads.
When training a model requires pulling historical tick data from one specialized database and massive text corpus embeddings from another, the resulting data transfer bottlenecks stall GPU clusters and delay time to market.
The destination: Unified, future-proof data foundations
To survive this shift, the market is heading away from complex, stitched-together legacy stacks and toward unified, simpler data foundations.
The goal is an architecture that can natively store, index, and serve both high-frequency structured data and massive unstructured data sets. By consolidating the data layer, firms can satisfy today’s stringent, low-latency performance demands while inherently supporting the massive throughput required for next-generation AI. Ultimately, the winners will be the firms that build a foundation capable of evolving alongside AI models, eliminating the need for constant, disruptive architectural reinvestment.
What this means for quant teams
Over the next year, quant teams’ competitive advantage will be won or lost based on how effectively they can remove friction between data, compute, and iteration cycles.
The firms pulling ahead are hiring better researchers, but more importantly, they’re giving these researchers an environment where they can test more ideas against more data faster than anyone else. This means prioritizing unified, high-performance data platforms that eliminate bottlenecks in backtesting, model development, and real-time execution. It also means investing in architectures that can scale with both structured and unstructured data without introducing complexity.
Forget incremental improvements. The real advantage is compressing the time from idea to production. Firms that iterate faster have a better chance of generating alpha.
Where Everpure fits
At Everpure, we believe competitive advantage is won or lost in the speed of the iteration cycle. If you can’t efficiently ingest, process, and serve massive, diverse data sets to your models, you aren’t competing on your AI capabilities because you’re architecturally constrained.
Everpure provides the unified, high-performance data foundation that eliminates infrastructure bottlenecks. By simplifying data architectures and unifying structured and unstructured data, we help quant teams keep their compute resources fully utilized, drastically reduce time to insight, and accelerate the path from a research idea to live production trading.
Learn how leading quant teams are modernizing data platforms to accelerate research, backtesting, and AI-driven trading workflows. Download the white paper.
