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Node Types
We have configured a "Standard Instance" that we have found to be useful for most workloads for each GPU type offered on CoreWeave Cloud. These instances are a starting point, but can be configured entirely to suit your use case or compute needs.
You can view Standard Instance configurations on the pricing page.
For more information about the à la carte pricing of compute components on CoreWeave Cloud, click below.

When custom configuring your instances on CoreWeave Cloud, the following table outlines the physical limitation of how many GPUs are available per instance.
Class
Generation
VRAM
Max per Instance
Label
A00 NVLINK
Ampere
80 GB
8
A100_NVLINK_80GB
A100 NVLINK
Ampere
40 GB
8
A100_NVLINK
A100 PCIe
Ampere
40 GB
8
A100_PCIE_40GB
A100 PCIe
Ampere
80 GB
8
A100_PCIE_80GB
A40
Ampere
48 GB
8
A40
A6000
Ampere
48 GB
8
RTX_A6000
RTX A5000
Ampere
24 GB
8
RTX_A5000
RTX A4000
Ampere
16 GB
7
RTX_A4000
Tesla V100 NVLINK
Volta
16 GB
8
Tesla_V100_NVLINK
RTX 5000
Turing
16 GB
4
Quadro_RTX_5000
RTX 4000
Turing
8 GB
7
Quadro_RTX_4000
If a workload requests more peripheral compute resources (vCPU, RAM) than offered in a standard instance size, additional costs will incur.

CPU Only nodes are available for tasks such as control-plane services, databases, ingresses and CPU rendering.
CPU Model
Max RAM per vCPU
Max vCPU per Workload
Label
Intel Xeon v3
4 GB
70
intel-xeon-v3
Intel Xeon v4
4 GB
70
intel-xeon-v4
Intel Xeon Scalable
4 GB
94
intel-xeon-scalable
AMD Epyc Rome
4 GB
46
amd-epyc-rome
AMD Epyc Milan
4 GB
46
amd-epyc-milan
Workloads without GPU requests are always scheduled on CPU nodes.

A combination of resource requests and node affinity is used to select the type and amount of compute for your workload. CoreWeave Cloud relies only on these native Kubernetes methods for resource allocation, allowing maximum flexibilty. The label used to select GPU type is gpu.nvidia.com/class and CPU type is node.coreweave.cloud/cpu. Please note that these labels are mutually exclusive, CPU type can't be explicitly selected for GPU nodes.
Single A100 80GB
8x A100 NVLINK
A100 With Fallback To A40
16 Core Xeon v3/v4 CPU
Single Epyc CPU
spec:
containers:
- name: example
resources:
limits:
cpu: 15
memory: 97Gi
nvidia.com/gpu: 1
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values:
- A100_PCIE_80GB
spec:
containers:
- name: example
resources:
requests:
cpu: 90
memory: 700Gi
limits:
nvidia.com/gpu: 8
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values:
- A100_NVLINK
spec:
containers:
- name: example
resources:
limits:
cpu: 12
memory: 24Gi
nvidia.com/gpu: 1
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values:
- A100_PCIE_40GB
- A40
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 20
preference:
matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values:
- A100_PCIE_40GB
spec:
containers:
- name: example
resources:
limits:
cpu: 16
memory: 48Gi
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: node.coreweave.cloud/cpu
operator: In
values:
- intel-xeon-v3
- intel-xeon-v4
spec:
containers:
- name: example
resources:
limits:
cpu: 1
memory: 4Gi
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: node.coreweave.cloud/cpu
operator: In
values:
- amd-epyc-rome
- amd-epyc-milan
Kubernetes allows resources to be scheduled with requests and limits. When only limits are specified, the requests are set to the same amount as the limit.
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Component Availability
CPU Availability
Requesting Compute in Kubernetes