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 V100 NVLINK
Volta
5,120
16 GB
Tesla_V100_NVLINK
NVIDIA
Tesla V100
Volta
5,120
16 GB
Tesla_V100
NVIDIA
Multi Purpose Turing
Turing
2,000+
8+ GB 
NV_Turing
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.
Class
vCPU
RAM
Great For
Tesla V100 NVLINK
4 Xeon Silver
32 GB
Deep learning, neural network training, HPC
Tesla V100
3
20 GB
AI inference, rendering, batch processing, hashcat
Mutli Purpose Turing
3
16 GB
Machine learning, rendering, batch processing
Tesla P100 NVLINK
4 Xeon Silver
32 GB
Entry level HPC, rendering, batch processing
Multi Purpose Pascal
1
8 GB
Video transcoding, rendering, batch processing
If a workload requests more peripheral compute resources (vCPU, RAM) than offered in a standard instance size, additional costs will incur. 
Please reach out to cloud.support@coreweave.com for additional information on enhanced vCPU/RAM combinations and their costs.
CPU Availability
CPU Model
RAM per vCPU
Label
Intel Xeon v1
3 GB
xeon
AMD Epyc Rome
4 GB
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.

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Useful Commands

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Advanced Label Selectors

Last updated 4 weeks ago

Vendor	Class	Generation	CUDA Cores	VRAM	Label
NVIDIA	Tesla V100 NVLINK	Volta	5,120	16 GB	Tesla_V100_NVLINK
NVIDIA	Tesla V100	Volta	5,120	16 GB	Tesla_V100
NVIDIA	Multi Purpose Turing	Turing	2,000+	8+ GB	NV_Turing
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 V100 NVLINK	4 Xeon Silver	32 GB	Deep learning, neural network training, HPC
Tesla V100	3	20 GB	AI inference, rendering, batch processing, hashcat
Mutli Purpose Turing	3	16 GB	Machine learning, rendering, batch processing
Tesla P100 NVLINK	4 Xeon Silver	32 GB	Entry level HPC, rendering, batch processing
Multi Purpose Pascal	1	8 GB	Video transcoding, rendering, batch processing

CPU Model	RAM per vCPU	Label
Intel Xeon v1	3 GB	xeon
AMD Epyc Rome	4 GB	epyc