AI Model Training | CoreWeave Solutions

AI model training at scale

CoreWeave helps teams train AI models faster by combining high-performance infrastructure with reliable orchestration and AI developer tools—so you can scale from early experiments to production without rebuilding your stack.

When training slows, progress stalls

Training frontier models is high-stakes. Talent is scarce, timelines are tight, and every delay slows progress. Yet teams still lose weeks duplicating experiments, managing fragmented workflows, and recovering from avoidable disruptions.

When iteration slows and reliability breaks down, even the best research teams struggle to move fast enough to stay ahead.

Why AI model training hits bottlenecks

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Compute inefficiency wastes training cycles

Large-scale training is expensive, yet much of that cost is lost to underutilized GPUs and inefficient scheduling. When compute sits idle or runs below capacity, teams complete fewer experiments, slow iteration, and burn budget without translating spend into meaningful model progress.

Failures disrupt long-running training jobs

Long-running training jobs are fragile at scale. Infrastructure faults, node failures, and slow recovery interrupt progress, often with limited visibility into root causes. Without clear observability, teams lose days of compute to restarts and stalled runs, making reliability a critical constraint for large, complex training workloads.

Teams struggle to iterate quickly and reproduce results

Teams struggle with experimental chaos. They are often managing a fragmented ecosystem where model versions are lost, results aren't reproducible, and critical knowledge resides in individual notebooks.

The CoreWeave Effect

20% faster

Get breakthroughs to market faster

96% goodput

More model progress per dollar

100K+ experiments

Visualize experiments at any scale

Optimized workflows that streamline training pipelines

Run experiments at scale, analyze them interactively, and quickly deploy higher-quality models. Centrally track models, datasets, metadata, and their lineage in the W&B Registry to strengthen governance and reproducibility and support CI/CD. Automate workflows for training, evaluation, and deployment so your teams can iterate rapidly with confidence.

CoreWeave platform for AI model training

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GPU Compute

Run distributed workloads with predictable performance and full control as your experiments scale into production. Purpose-built cloud infrastructure for training and running large AI models, CoreWeave provides bare metal access to the latest architectures.

CoreWeave Mission Control

Monitor training runs, diagnose issues, and manage large-scale infrastructure with confidence. The operating standard for running AI on CoreWeave Cloud, Mission Control provides unified visibility into GPU, network, and storage health.

SUNK (Slurm on Kubernetes)

Run distributed workloads efficiently, isolating failures and managing GPU resources across complex research environments. SUNK is an AI-native research cluster designed for large-scale, distributed model training, combining Slurm scheduling with Kubernetes orchestration.

CKS (CoreWeave Kubernetes Service)

Reduce overhead while preserving flexibility with preconfigured clusters, high-performance networking and storage, and managed operations. CKS is a managed Kubernetes service optimized for AI workloads to provide a cloud-native environment for distributed training and experimentation.

CoreWeave AI Object Storage

Simplify data management and ensure consistent access to large-scale training data throughout the model lifecycle. A high-performance object storage system built for AI training pipelines, CoreWeave AI Object Storage provides a single, global dataset accessible across clusters.

Explore the CoreWeave Cloud Platform

Build, tune, and manage AI models with Weights & Biases

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CoreWeave moves fast—constantly expanding the platform with new capabilities. The acquisition of Weights & Biases brings best-in-class AI development tools directly into our stack, empowering researchers and engineers to develop AI agents and models. Trusted by over 1,500 teams, including 30+ foundation model builders, Weights & Biases helps AI teams iterate faster to deliver real-world impact.

With Weights & Biases, you can:

Pre-train and post-train LLMs for agentic tasks
Evaluate, iterate, monitor, and safeguard agents
Tap into enterprise-grade performance, scale, governance, and security

W&B Models helps teams build, tune, and manage AI models from experimentation to production. It boosts experiment speed and team collaboration to bring models to production faster while ensuring unparalleled performance, trusted data reliability, and enterprise-grade security.

W&B Experiments

Track, compare, and visualize your experiments. Quickly implement experiment tracking by adding just a few lines to your training script and improve reproducibility, collaboration, and productivity.

W&B Registry

Publish, share, and manage your AI models and datasets throughout their lifecycle. Registry is a central repository that provides versioning, lineage tracking, and governance of model artifacts.

W&B Automations

Streamline your AI pipeline and implement CI/CD by automating workflow steps so lifecycle events automatically trigger the right downstream actions.

Mistral AI moves 2.5x faster, trains smarter

Powerful open-open source LLMs launched at unprecedented speed

Learn how Mistral AI trains LLMs on reliable and resilient infrastructure that scales at the speed of AI while ensuring experiments are trackable and governed from experimentation through production.

Read the story

100%

Trained on CoreWeave Cloud

2.5x faster training

On NVIDIA BG200s

Trusted

Direct-to-expert support

Frequently Asked Questions

What makes CoreWeave Cloud smarter and faster for model training?

CoreWeave is purpose-built for large-scale AI workloads, not retrofitted cloud compute. Our bare-metal NVIDIA GPU clusters, dual-fabric network architecture, and intelligent orchestration deliver higher utilization, faster iteration, and dramatically lower latency than legacy cloud providers.

How does Weights & Biases integrate with CoreWeave?

Weights & Biases is natively integrated into CoreWeave’s platform, giving researchers and engineers a unified environment to train, track, and productionize models at scale.

What kind of performance gains are your customers experiencing?

CoreWeave customers typically see up to 20% higher GPU utilization and 96% goodput, meaning almost every GPU dollar translates directly into model progress. Faster restarts, higher throughput, and smarter scheduling keep experiments running at full speed.

How does CoreWeave ensure reliability and improve uptime?

Through automated node monitoring, rapid re-queuing, and our custom SLURM on Kubernetes (SUNK) system, CoreWeave keeps your training jobs running smoothly. Failed processes restart in ~90 seconds, compared to the 4+ minute average on traditional infrastructure.

What security and compliance measures are in place?

CoreWeave and Weights & Biases are built with security engineered for scale, including workload isolation, encrypted data transfer and storage, role-based access controls, and full compliance with enterprise standards. Your data and models stay protected throughout their lifecycle.

Who’s using Weights & Biases today?

Over 1,500 teams, including 30+ foundation model builders, rely on Weights & Biases. From frontier research labs to production-scale AI companies, the world’s top builders trust this stack to move faster and scale smarter.

Webinar

How to measure and optimize AI infrastructure for large-scale training

Join Distinguished Engineer Wes Brown and Product Manager Deok Filho as they pull back the curtain on the methodology, the surprises, and the hard-won optimizations that delivered up to 20% more throughput, 10x longer uptime, and 97% utilization for large scale training.

Watch on demand