In quantitative (quant) trading, fractional differences in model accuracy can result in significant alpha gain or decay, making reliable models a defining competitive edge. But existing tools and techniques simply weren’t designed to build models at today’s scale, and old workflows—where researchers build the model and hand it off, then engineers optimize for latency and cost—struggle to keep up.

Traditional quant models relied on curated data, researchers’ own intuition and experience, and relatively simple mathematical techniques like linear regression. Now, quants are looking more and more like leading AI labs, training and deploying LLMs and larger, more advanced AI models to support their strategies. More common now are generalized models that ingest internet-scale financial data, adapt in real time, and consistently make high quality decisions under rigorous governance. That approach requires investments in modern AI tools and purpose-built AI infrastructure critical to maintaining pace.

Compute speed is the new alpha

Embracing these advances in model training means expanding the industry’s definition of speed. Historically, speed has meant minimizing latency—firms won with shorter cables, colocated servers, and offices built next door to exchange data centers. Now, speed also means building, training, and deploying models faster than your competitor, and doing so using the world’s most powerful GPUs. It also means leaning on advanced training techniques like reinforcement learning (RL) to improve data handling and context-aware execution, and using LLMs to judge predictions.

The requirements for building quant models and LLMs are very similar: 

• Access to internet-scale data that must be processed and acted upon in real-time
• Constant user interactions with the model
• Governance and security built in to protect sensitive data and IP
• Massive compute capacity for intensive training cycles
• Low-latency, high-throughput infrastructure

To gain the edge through infrastructure, leading quant firms like Jane Street have built their models using purpose-built AI cloud infrastructure from CoreWeave and companies like LG AI Research and Riskfuel use model training, fine-tuning and management tools from Weights & Biases.

Building a scalable, secure, and high-performance AI cloud

CoreWeave Cloud, the Essential Cloud for AI, is purpose-built to provide the speed, scale, and reliability you need for bleeding edge quant model training and inference. Quant firms prize control, determinism, latency, and deep observability, and have historically gotten that through finely tuned on-prem environments. To succeed in cloud infrastructure today, they need:

• A new level of predictability and insight
• Performance on par with or better than on-prem
• Unprecedented operational control
• Transparent costs and the elimination of hidden fees

CoreWeave Cloud directly targets these friction points. It’s the only cloud to ever earn Platinum status in SemiAnalysis’s ClusterMAX™, the world’s first independent AI cloud rating system, and it’s the only cloud to do it twice.

With the unified power of CoreWeave and Weights & Biases, quant firms can now collaborate easier and train and serve models faster. CoreWeave Cloud is purpose-built for AI and offers flexible consumption models with on-demand and reserved capacity designed to match your unique project needs. Combined with the powerful unified dashboard in Weights & Biases, today’s quant firms have a new source of competitive advantage designed to give them the winning edge.

CoreWeave Mission Control™ and Weights & Biases bring metal-to-token observability to training workflows 

CoreWeave Mission Control™ is the industry’s first operating standard for AI at scale. It unifies security, expert-led operations, and observability that allows your teams to see clearly, act precisely, and run at full speed with confidence.

Data, insights, and remediation recommendations from Mission Control are directly visible in W&B Models alongside training metrics. This integration boosts productivity and improves efficiency by allowing you to:

• Correlate infrastructure events directly with training runs
• Simplify diagnostics for issues like node failures, hardware errors, and networking timeouts
• Improve the accuracy of your models while optimizing infrastructure behavior

Mission Control’s deep observability provides the granularity and control of on-prem with the conveniences of cloud.

CoreWeave AI Object Storage: Data as dynamic as the models it powers

Slow storage is often a bottleneck for training jobs. Loading datasets and saving intermediate model checkpoints can be slow and unstable, and as data slows access across multiple runs, it can accumulate into weeks of unnecessary (and costly) downtime.

For quant firms building in the cloud, data mobility is a baseline requirement. That‘s why we built CoreWeave AI Object Storage, the industry-leading, fully managed storage service purpose-built for AI. Powered by Local Object Transport Accelerator (LOTA) technology, CoreWeave AI Object Storage eliminates the friction of moving data between regions, clouds, and tiers. It combines simplicity, scalability, and transparency, offering throughput of up to 7 GB/s per GPU, zero fees (egress, ingress, or request), and automated, usage-based billing levels that cut existing customers’ costs by more than 75%.

CoreWeave AI Object Storage can be used with Weights & Biases to store AI artifacts so you can:

• Rapidly load datasets and save checkpoints as fast as possible, optimizing valuable GPU resources and engineering time
• Improve experiment velocity with accelerated data access for large-scale training and fine-tuning jobs
• Securely store artifacts and other sensitive data directly in your existing CoreWeave cloud environment

Streamline the quant research loop with Weights & Biases

To accelerate research, optimize performance, and deploy models with confidence, quant researchers use tools from Weights & Biases. Weights & Biases helps teams train, fine-tune, govern, and manage models from experimentation to production, accelerating time to market. Weights & Biases boosts experiment speed, productivity, and collaboration while ensuring model performance and security.

W&B Models

Quant researchers train, govern, and manage models from prototype through production with W&B Models Experiment Tracking. Researchers can visualize and compare every experiment with model metrics streaming live into interactive graphs and tables.

W&B Registry

Quant researchers can centralize the tracking of models, datasets, metadata, and lineage within a unified W&B Registry. W&B Registry supports governance, reproducibility, and CI/CD best practices while automating key workflows for training, evaluation, and deployment.

W&B Reports

To document findings from training experiments and share them with teams and stakeholders, quant researchers use W&B Reports. W&B Reports tracks results dynamically, keeps everyone informed, gathers feedback, compares results across projects, sets benchmarks, and more.

CoreWeave x Weights & Biases: the new alpha for quantitative trading

CoreWeave and Weights & Biases help you build and operate faster, stay online, and iterate without friction. Because every second saved in debugging or retraining translates into faster signal validation and strategy deployment.

Want to learn more about quant’s shift to purpose-built AI cloud? Read the blog.

Ready to have a conversation about how CoreWeave and Weights & Biases can accelerate your quant research? Let’s talk.