GPU infrastructure for trading, risk modeling, fraud detection, and financial AI. Built for the latency, throughput, and operational control that trading firms, asset managers, and fintechs operate under.
Financial firms are running GPU compute against problems where the cost of slow infrastructure is measured in basis points. Strategies that backtested in days now iterate in hours. Risk models that ran overnight on CPU clusters now finish before the next trading session. Fraud detection that depended on rule engines now runs inference against full transaction streams in real time.
Train and deploy models for market prediction, signal generation, and automated execution. Built for environments where iteration speed is the moat and infrastructure latency is part of the alpha calculation.
Run Monte Carlo simulations, stress tests, and scenario analysis against full portfolios. Workloads that ran overnight on CPU clusters finish inside a trading session, which changes how often risk gets re-evaluated.
Deploy AI systems that score transactions in real time, catching anomalies before settlement instead of after the fact. GPU-accelerated inference handles the throughput volumes that rule-based systems were never designed for.
Apply machine learning to asset allocation, factor modeling, and strategy backtesting. GPU compute lets you test across larger datasets and more complex model architectures than traditional infrastructure makes practical.
Build and deploy large language models for filing analysis, earnings call summarization, compliance review, and internal research support. Train on proprietary data with full control over your model weights and your audit trail.
Trading firms, asset managers, and fintechs all have different P&L structures, regulatory posture, and utilization profiles that shape whether a capital expenditure or operating expenditure model makes more sense. Arc Compute supports both and helps you figure out which one fits.
Purchase your GPU systems outright and deploy them in your own facility, a colocation cage, or a proximity hosting environment. You own the hardware, control the deployment, and benefit from a lower cost per GPU hour as the systems run at sustained utilization.
Trading firms, quant shops, and asset managers with sustained, predictable compute demand and the operational maturity to run their own infrastructure at high utilization.
Access GPU infrastructure through managed services, leasing, or consumption-based models. You get high-performance compute without the upfront capital, with the ability to scale capacity up or down as strategies, market conditions, or AI initiatives evolve.
Fintechs, emerging managers, and AI-native teams with variable workloads or budget structures that favor operating expenses over capital outlays. Common where strategy iteration is faster than hardware procurement cycles.
Most firms run a mix. A quant shop might own its production trading infrastructure while leasing burst capacity for research and backtesting. An asset manager might own core risk infrastructure while accessing on-demand compute for ad-hoc scenario analysis. Arc Compute helps you design the right combination.
Dedicated GPU infrastructure with the flexibility of cloud, giving you full control over performance, cost, and data. Built for teams that need cloud-like agility with the data control and audit posture financial services requires.
Fully integrated GPU clusters for rapid deployment and scalable performance. Built for trading firms, asset managers, and fintechs standing up dedicated compute environments for production trading, research, or AI workloads.
Individual GPU servers configured for specific workloads, from single systems to large infrastructure builds. The right option when specific trading, research, or modeling workflows need specific hardware configurations.