> ## Documentation Index
> Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt
> Use this file to discover all available pages before exploring further.

# What are the recommended resource requests and limits for GPU Pods?

For most GPU training and inference workloads, set CPU and memory `requests` to roughly the per-GPU share of the instance, leaving headroom for DaemonSets and system overhead. For example, on an 8-GPU H100 Node (128 vCPUs, 2 TB of RAM), a per-GPU split of about 15 vCPUs and 225 GB derives from the official whole-Node example (`cpu: 120`, `memory: 1800Gi`) in [Target specific GPUs or CPUs](/products/cks/nodes/manage#target-specific-gpus-or-cpus). Set `limits` equal to `requests` for Guaranteed-QoS Pods, or higher than `requests` if your workload occasionally bursts. Specify GPUs under `limits` as `nvidia.com/gpu: 1` (or more). Kubernetes mirrors the value to `requests` automatically.

For background on how requests and limits behave on GPU Pods, see [How do CPU and memory requests work with GPU Pods?](/support/cks/articles/how-do-cpu-and-memory-requests-work-with-gpu-pods).

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<Badge stroke shape="pill" color="blue" size="md">[Workload Scheduling](/support/cks/tags/workload-scheduling)</Badge>