Accelerating AI innovation
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
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Purpose-Built Infrastructure
1:50
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.
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Low-Latency Storage Solution
4:00
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.
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Unified Operational Control
6:20
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.
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Accelerated Development Lifecycle
7:10
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
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CoreWeave AI Platform Stack
70s
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™).
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CoreWeave Kubernetes Service (CKS)
180s
CKS manages the bare metal infrastructure, providing customers with administrative control over their own Kubernetes cluster for easier scaling and deployment.
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Runtime Optimization
200s
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.
Mentioned resources
- CoreWeave AI Object Storage
- Local Object Transport Accelerator (LOTA)
- CoreWeave Kubernetes Service (CKS)
- Weights & Biases (W&B) Models
- Mission Control™
Channel & topics
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