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.