GPU INFRASTRUCTURE

A trading firm running low-latency inference has different infrastructure needs than a biotech company simulating molecular dynamics. An AI startup migrating off cloud needs a different engagement model than a private equity firm building a GPU cloud investment. The workloads are different, the compliance requirements are different, the timelines are different, and the economics are different.

Arc Compute brings the same core infrastructure expertise to every engagement, but how we apply it changes based on your industry. We have worked across all four of the sectors below and understand the specific constraints, workflows, and decision-making processes that shape GPU infrastructure decisions in each one.

Trading firms, asset managers, and fintech companies depend on GPU compute to run the models that drive their business. The performance, latency, and reliability of that infrastructure directly impacts outcomes. Arc Compute works with financial services organizations to deploy GPU infrastructure designed for speed, operational control, and regulatory compliance.

Arc Compute brings the same core infrastructure expertise to every engagement, but how we apply it changes based on your industry. We have worked across all four of the sectors below and understand the specific constraints, workflows, and decision-making processes that shape GPU infrastructure decisions in each one.

Key Workloads

Algorithmic and quantitative trading, risk modeling and simulation, fraud detection, portfolio optimization, financial AI and LLMs

Clients:

Healthcare and life sciences organizations are applying GPU-accelerated computing to problems that were previously too slow or too expensive to solve. Arc Compute works with research institutions, pharmaceutical companies, and biotech firms to deploy infrastructure that meets the performance demands and data privacy requirements of this space.

Key Workloads

Drug discovery and molecular simulation, genomics and sequencing analysis, medical imaging, clinical AI and NLP, protein structure prediction

Most AI startups begin on cloud GPUs, but as models get larger and inference volume grows, cloud spend becomes one of the biggest line items on the P&L. Arc Compute helps startups move to dedicated infrastructure that reduces compute costs, improves performance, and gives them full control over their stack.

Key Workloads

Foundation model training, production inference at scale, fine-tuning and model iteration, GPU cloud and resale, AI-powered products and platforms

Clients:

Demand for GPU compute is growing faster than supply. For private equity firms and investor groups, this creates a clear opportunity: deploy capital into GPU infrastructure, generate recurring revenue by selling compute to the market, and build a real asset with tangible hardware backing it. Arc Compute handles the full lifecycle from procurement through monetization.

Key Models

GPU Neo Cloud platforms, dedicated compute for portfolio companies, build-to-suit infrastructure, infrastructure acquisition and expansion

Clients: