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

Generation

Model

VRAM

Label

NVIDIA

Volta

Tesla V100

16 GB

Tesla_V100

NVIDIA

Turing

RTX 2080 Ti

11 GB

GeForce_RTX_2080_Ti

NVIDIA

Turing

RTX 2060 Super

8 GB

GeForce_RTX_2060_Super

NVIDIA

Pascal

GTX 1080 Ti

11 GB

GeForce_GTX_1080_Ti

NVIDIA

Pascal

GTX 1070 Ti

8 GB

GeForce_GTX_1070_Ti

NVIDIA

Pascal

GTX 1070

8 GB

GeForce_GTX_1070

NVIDIA

Pascal

P104-100

8 GB

P104-100

NVIDIA

Pascal

GTX 1060

6 GB

GeForce_GTX_1060_6GB

NVIDIA

Pascal

P106-100

6 GB

P106-100

System Resources

Each GPU includes a certain amount of host CPU and RAM, these are included at no additional fee.

GPU Model

vCPU

RAM

Great For

Tesla V100 NVLINK

4 Xeon Gold

32 GB

Deep learning, neural network training, HPC

Tesla V100

3

16 GB

AI inference, rendering, batch processing, hashcat

RTX 2080 Ti

3

16 GB

Machine learning, neural network training, HPC

RTX 2060 Super

3

16 GB

Machine learning, rendering, batch processing

GTX 1080 Ti

1

11 GB

Machine learning, rendering, batch processing

GTX 1070 Ti

1

8 GB

Video transcoding, rendering, batch processing

GTX 1070

1

8 GB

Video transcoding, rendering, batch processing

P104-100

0.5

8 GB

Batch processing, blockchain compute, hashcat

GTX 1060

0.5

6 GB

Video transcoding, batch processing

P106-100

0.5

6 GB

Batch processing, blockchain compute

A workload requesting more resources than allowed for the specific GPU class will have its resources capped to the maximum allowable amount. For example, launching a Pod with a request for 2 1070Tis will have its resource request capped to 2 CPU and 16GB RAM.

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
Dual GeForce 1070 (Ti)
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/model
operator: In
values:
- Tesla_V100
Dual GeForce 1070 (Ti)
spec:
containers:
- name: example
resources:
limits:
cpu: 2
memory: 16Gi
nvidia.com/gpu: 2
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/model
operator: In
values:
- GeForce_GTX_1070
- GeForce_GTX_1070_Ti
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.