Node Types

A wide range of GPU options are available, allowing you to select the most optimal compute resource for your workload. If GPU resources are not requested, the workload will be executed on a CPU only node.
GPU Availability
Vendor
Class
Generation
CUDA Cores
VRAM
Label
NVIDIA
Tesla A100 NVLINK
Ampere
6,912
40GB
Tesla_A100_NVLINK
NVIDIA
Tesla V100 NVLINK
Volta
5,120
16 GB
Tesla_V100_NVLINK
NVIDIA
Tesla V100
Volta
5,120
16 GB
Tesla_V100
NVIDIA
RTX 6000
Turing
4,608
24 GB
Quadro_RTX_6000
NVIDIA
RTX 5000
Turing
3,072
16 GB
Quadro_RTX_5000
NVIDIA
RTX 4000
Turing
2,304
8 GB 
Quadro_RTX_4000
NVIDIA
Tesla P100
Pascal
3,584
16 GB
Tesla_P100_NVLINK
NVIDIA
Multi Purpose Pascal
Pascal
2,000+
8 GB
NV_Pascal
System Resources
Each GPU includes a certain amount of host CPU and RAM, these are included at no additional fee. Allocating multiple GPUs to a single workload will increase the CPU and RAM allocation proportionally.
Class
vCPU
RAM
Great For
Tesla A100 NVLINK
8 Xeon Gold
96 GB
Complex Deep Neural Network training, HPC
Tesla V100 NVLINK
4 Xeon Silver
32 GB
Deep Neural Network training, HPC
Tesla V100
3
20 GB
AI inference, Rendering, Batch processing, Hashcat
RTX 6000
4 Xeon Silver
64 GB
Complex DNN Training, Rendering, Batch processing
RTX 5000
4 Xeon Silver
32 GB
Machine learning, Rendering, Batch processing
RTX 4000
3
16 GB
Machine learning, Rendering, Game streaming
Tesla P100 NVLINK
4 Xeon Silver
32 GB
Entry level HPC, Rendering, Batch processing
Multi Purpose Pascal
1
8 GB
Transcoding, Rendering, Game streaming, Batch
If a workload requests more peripheral compute resources (vCPU, RAM) than offered in a standard instance size, additional costs will incur. 
Additional CPU and RAM is billed in increments of $0.07/hr for 1 vCPU + 8 GB RAM.
CPU Availability
CPU Only nodes are available for tasks such as control-plane services, databases, ingresses and CPU rendering. 
CPU Model
RAM per vCPU
Max CPU per Workload
Label
Intel Xeon v1/v2
3 GB
78
xeon
AMD Epyc Rome
4 GB
46
epyc
Workloads without GPU requests are always scheduled on CPU nodes. If a specific CPU model is not explicitly selected, the scheduler will automatically schedule workloads requesting few CPU cores on Epyc class CPUs, as these perform exceptionally well on single thread workloads.
Requesting Compute in Kubernetes
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.
Single Tesla V100
4x Tesla V100 NVLINK
Dual Pascal
16 Core Xeon CPU
Single Epyc CPU
Single Tesla V100
spec:
  containers:
  - name: example
    resources:
      limits:
        cpu: 3
        memory: 16Gi
        nvidia.com/gpu: 1
        
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: gpu.nvidia.com/class
            operator: In
            values:
              - Tesla_V100
4x Tesla V100 NVLINK
spec:
  containers:
  - name: example
    resources:
      limits:
        cpu: 15
        memory: 128Gi
        nvidia.com/gpu: 4
        
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: gpu.nvidia.com/class
            operator: In
            values:
              - Tesla_V100_NVLINK
Dual Pascal
spec:
  containers:
  - name: example
    resources:
      limits:
        cpu: 2
        memory: 16Gi
        nvidia.com/gpu: 2
        
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: gpu.nvidia.com/class
            operator: In
            values:
              - NV_Pascal
16 Core Xeon CPU
spec:
  containers:
  - name: example
    resources:
      limits:
        cpu: 16
        memory: 48Gi
        
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: cpu.coreweave.cloud/family
            operator: In
            values:
              - xeon
Single Epyc CPU
spec:
  containers:
  - name: example
    resources:
      limits:
        cpu: 1
        memory: 4Gi
        
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: cpu.coreweave.cloud/family
            operator: In
            values:
              - epyc
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.

CoreWeave Kubernetes - Previous

Useful Commands

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Advanced Label Selectors

Last updated 1 month ago

Vendor	Class	Generation	CUDA Cores	VRAM	Label
NVIDIA	Tesla A100 NVLINK	Ampere	6,912	40GB	Tesla_A100_NVLINK
NVIDIA	Tesla V100 NVLINK	Volta	5,120	16 GB	Tesla_V100_NVLINK
NVIDIA	Tesla V100	Volta	5,120	16 GB	Tesla_V100
NVIDIA	RTX 6000	Turing	4,608	24 GB	Quadro_RTX_6000
NVIDIA	RTX 5000	Turing	3,072	16 GB	Quadro_RTX_5000
NVIDIA	RTX 4000	Turing	2,304	8 GB	Quadro_RTX_4000
NVIDIA	Tesla P100	Pascal	3,584	16 GB	Tesla_P100_NVLINK
NVIDIA	Multi Purpose Pascal	Pascal	2,000+	8 GB	NV_Pascal

Class	vCPU	RAM	Great For
Tesla A100 NVLINK	8 Xeon Gold	96 GB	Complex Deep Neural Network training, HPC
Tesla V100 NVLINK	4 Xeon Silver	32 GB	Deep Neural Network training, HPC
Tesla V100	3	20 GB	AI inference, Rendering, Batch processing, Hashcat
RTX 6000	4 Xeon Silver	64 GB	Complex DNN Training, Rendering, Batch processing
RTX 5000	4 Xeon Silver	32 GB	Machine learning, Rendering, Batch processing
RTX 4000	3	16 GB	Machine learning, Rendering, Game streaming
Tesla P100 NVLINK	4 Xeon Silver	32 GB	Entry level HPC, Rendering, Batch processing
Multi Purpose Pascal	1	8 GB	Transcoding, Rendering, Game streaming, Batch

CPU Model	RAM per vCPU	Max CPU per Workload	Label
Intel Xeon v1/v2	3 GB	78	xeon
AMD Epyc Rome	4 GB	46	epyc