Flexible Capacity Models

6 posts · May 2026

What this trail covers

CoreWeave enhances its AI infrastructure with Flexible Capacity Plans, introducing Flex Reservations and Spot instances to better align with fluctuating AI workload demands. This post details 'Burst Compute', a model for on-demand GPU scaling across thousands of GPUs, enabling instant scaling up and down for workloads like batch simulations, inference, and VFX rendering. It highlights rapid spin-up times (as little as 5 seconds) enabled by container image caching and specialized schedulers.

All Coreweave trails

May 2026Breaking the Bottlenecks: Scaling AI Without Stalling | CoreWeave BlogThis post details strategies for scaling AI infrastructure without stalling, focusing on addressing compute starvation, utilization, networking constraints, and storage drag. It advocates for designing for GPU elasticity, architecting for inevitable bursts, unifying observability across GPU, network, and storage layers, bringing compute closer to data, and designing for resilient multi-cloud, multi-AZ inference. It highlights the benefits of purpose-built AI clouds with GPU diversity, high-bandwidth interconnects, AI-tuned storage, and Kubernetes-native orchestration. Key trends include embedded automation and observability, edge inference, and multi-cloud/open-source strategies.
May 2026Bring Back AI Cloud Elasticity with CoreWeave Flex | CoreWeave BlogThis post introduces CoreWeave Flexible Capacity Plans, specifically 'Flex Reservations' (guaranteed capacity with a holding fee for variable workloads) and 'Spot' instances (lower-cost, interruptible capacity with preemption signaling). It positions these as closing the gap between existing Reservations and On-Demand options, aiming to provide a controllable way to match cost and certainty to AI workload demands. The post also mentions the introduction of 'Dedicated Inference' for platform teams and reiterates the use of Serverless Inference in Weights & Biases and CoreWeave Kubernetes Services for inference workloads, emphasizing the portfolio approach to AI capacity from proof to production.
May 2026Burst Computing Across Thousands of GPUs in the Cloud | CoreWeaveThis post introduces and elaborates on 'Burst Compute' as a core capability for scaling workloads across thousands of GPUs instantly. It details how CoreWeave's Kubernetes-native infrastructure, leveraging components like Knative, Keda, and Argo Workflows, enables rapid auto-scaling for inference, batch processing, and rendering. Key technical enablers highlighted include container image caching and specialized schedulers, which contribute to spin-up times as low as 5 seconds. The post also emphasizes the cost-efficiency and practical access to scale provided by this model, contrasting it with legacy cloud infrastructure limitations.
May 2026CoreWeave Empowers Real-Time Collaborative Filmmaking with Spire Animation StudiosThis post details how Spire Animation Studios leverages CoreWeave's Kubernetes-native platform and VS API to provision and manage virtual workstations with high-end NVIDIA GPUs for their real-time collaborative filmmaking workflows. It highlights the ability to dynamically change VM specifications, spin up short-lived machines for specific tasks, and scale rendering workers on demand (from 0 to 1,000 within a minute). The integration with Unreal Engine and Pixel Streaming for interactive iteration and remote review is also discussed, showcasing the flexibility and scalability of CoreWeave's infrastructure for demanding animation production.
May 2026Flex Reservations and Spot Explained | CoreWeave BlogThis post introduces and explains CoreWeave's Spot and Flex Reservations capacity models. Flex Reservations separate guaranteed access from 24/7 run-rate pricing by offering a lower holding fee for reserved capacity and a usage rate only when nodes are actively in use. Spot instances are presented as lower-cost, interruptible compute with explicit termination signaling and a defined preemption notice window to allow for checkpointing. The post also highlights CoreWeave ARENA as a platform for testing Spot instances and mentions Weights and Biases Inference for production inference workflows.
Advertisement
May 2026How Autoscaling Impacts Compute Costs for Inference | CoreWeaveThis post details how autoscaling, specifically focusing on faster model loading times and pod spin-up times, directly impacts compute costs for inference workloads. It presents three scenarios (no autoscaling, slow autoscaling, fast autoscaling) to illustrate the cost-efficiency benefits of responsive autoscaling without performance degradation. Key factors influencing autoscaling quality are identified as pod spin-up time, model load time, compute quotas, virtualization overhead, GPU type, and networking. The post highlights CoreWeave's advantage in rapid instance spin-up (8-10x faster than generalized clouds) due to its bare-metal infrastructure and optimized autoscaling.
May 2026Our Capacity Plans for CoreWeave Data Centers | CoreWeaveThis post details CoreWeave's capacity plans for its data centers, focusing on addressing the challenges of increased rack power consumption with liquid-cooled infrastructure supporting ~130kW racks, and the limitations of traditional data center designs for AI workloads through purpose-built facilities and strategic vendor partnerships. It also highlights the prioritization of maximum performance and resilience through optimized storage (LOTA), networking (NVIDIA Quantum-2 InfiniBand), and automated fleet/node lifecycle management. The post further discusses the need for access to dynamic, burstable compute capacity.