# Accelerating AI innovation

## Executive summary

CoreWeave presents its purpose-built, end-to-end AI platform designed to manage complex AI workloads from training to inference. The stack integrates specialized foundational infrastructure (high-density GPU clusters, high-speed interconnects) with advanced software layers, including CoreWeave AI Object Storage (using LOTA) and CoreWeave Kubernetes Service (CKS). Operational control is unified through Mission Control™, providing observability and management across the entire lifecycle, from model development (W&B) to deployment.

## Key takeaways

- Purpose-Built Infrastructure: CoreWeave manages data centers optimized specifically for high-density GPUs and high-speed interconnects, scaling across multiple regions. The bare metal infrastructure is managed via Kubernetes, supplemented by services like Slurm for cluster management.
- Low-Latency Storage Solution: The CoreWeave AI Object Storage solution utilizes the Local Object Transport Accelerator (LOTA). Each GPU node runs a LOTA proxy, which caches the object storage across the cluster, achieving throughput of up to 7 GB/s per GPU.
- Unified Operational Control: Mission Control™ unifies security, observability, and talent services into a single pane of glass, providing real-time visibility into job state, cluster health, and performance signals, helping teams diagnose issues proactively.
- Accelerated Development Lifecycle: The platform integrates W&B Models at the top layer, shortening the path to production by providing integrated experiment tracking, governance, and workflow tooling for model and agent development.

## Technical details

- CoreWeave AI Platform Stack: The stack is layered: Foundational Infrastructure (data centers, GPU clusters) -> Storage (CoreWeave AI Object Storage/LOTA) -> Control (CoreWeave Kubernetes Service/Slurm) -> Runtime Acceleration (optimizing training/inference) -> Application Layer (W&B Models, Mission Control™).
- CoreWeave Kubernetes Service (CKS): CKS manages the bare metal infrastructure, providing customers with administrative control over their own Kubernetes cluster for easier scaling and deployment.
- Runtime Optimization: Runtime acceleration focuses on reducing startup latency, improving throughput, and increasing utilization. Examples include using Slurm on Kubernetes or specialized implementations like 'sunk' for streamlined workload deployment.

## Practical implications

- Build engineers can leverage the integrated W&B Models layer to shorten the path from experimentation to production, focusing on workflow tooling and governance.
- The combination of CKS and Slurm provides familiar, robust control planes for managing complex, high-density GPU clusters.
- Mission Control™ reduces operational overhead by unifying observability signals (cluster health, efficiency, performance) into a single view, allowing for proactive issue detection and minimizing GPU downtime.

## Topics

AI Infrastructure, Cloud Computing, Kubernetes, MLOps, Distributed Storage, High-Performance Computing (HPC), CoreWeave AI Object Storage, Local Object Transport Accelerator (LOTA), CoreWeave Kubernetes Service (CKS), Weights & Biases (W&B) Models, Mission Control™

Source: https://www.youtube.com/watch?v=_SS42M8Raz0