CoreWeave

CoreWeave is a specialized GPU cloud provider built from the ground up for AI workloads, offering dedicated A100, H100, and H200 GPU clusters with high-bandwidth InfiniBand networking. It is designed for teams that need bare-metal GPU performance at lower cost than the major hyperscalers (AWS, GCP, Azure).

• Large-scale training — train foundation models across multi-node GPU clusters with NVLink and InfiniBand interconnects optimized for all-reduce operations
• Dedicated GPU allocation — reserve guaranteed GPU capacity without competing for spot instances or dealing with preemption
• Kubernetes-native — deploy and orchestrate AI workloads using standard Kubernetes tooling (kubectl, Helm) with GPU-aware scheduling
• Inference at scale — serve models on dedicated GPU nodes with auto-scaling, load balancing, and persistent storage
• Object storage — high-throughput S3-compatible storage co-located with GPU clusters for fast data loading during training

AI companies, research labs, and enterprises building foundation models or running large-scale inference choose CoreWeave because it offers 35-65% lower costs than equivalent hyperscaler GPU instances, with networking optimized specifically for distributed AI workloads.

CoreWeave is the infrastructure provider behind several high-profile AI companies and is a preferred partner for NVIDIA GPU deployments. It is best suited for organizations comfortable with Kubernetes that need sustained, large-scale GPU capacity rather than occasional spot usage.

1. Request access — sign up at coreweave.com and complete the registration process. CoreWeave uses an allocation-based model, so you will need approval for GPU capacity.
2. Set up kubectl — once approved, download your kubeconfig from the CoreWeave dashboard and configure your local environment:

   export KUBECONFIG=~/.kube/coreweave-config
   kubectl get nodes  # Verify connectivity

3. Deploy a GPU workload — create a Kubernetes pod with GPU resource requests:

   apiVersion: v1
   kind: Pod
   metadata:
     name: training-job
   spec:
     containers:
     - name: trainer
       image: your-registry/training:latest
       resources:
         limits:
           nvidia.com/gpu: 8
     nodeSelector:
       gpu.nvidia.com/class: H100_SXM

4. Persistent storage — attach high-performance storage volumes for datasets and checkpoints using CoreWeave's storage classes.
5. Container registry — push your Docker images to CoreWeave's private registry or use images from public registries like NGC.

Pricing: H100 SXM: ~$2.23/hr per GPU. A100 80GB: ~$2.06/hr per GPU. Pricing is significantly lower than equivalent AWS/GCP instances. Reserved capacity contracts offer further discounts. Full pricing details.