Coreweave cost tuning
'Optimize CoreWeave GPU cloud costs with right-sizing and scheduling.From its SKILL.md
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill coreweave-cost-tuningAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its file declares
Copied from the file, not written here
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
2.2 KB, 473 tokens by cl100k_base, as published. Nobody here has run it
CoreWeave Cost Tuning
Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
GPU Pricing Reference (approximate)
| GPU | Per GPU/hour | Best For |
|---|---|---|
| A100 40GB PCIe | ~$1.50 | Development, smaller models |
| A100 80GB PCIe | ~$2.21 | Production inference |
| H100 80GB PCIe | ~$4.76 | High-throughput inference |
| H100 SXM5 (8x) | ~$6.15/GPU | Training, multi-GPU |
| L40 | ~$1.10 | Image generation, light inference |
Cost Optimization Strategies
Scale-to-Zero for Dev/Staging
autoscaling.knative.dev/minScale: "0"
autoscaling.knative.dev/scaleDownDelay: "5m"
Right-Size GPU Selection
def recommend_gpu(model_size_b: float, inference_only: bool = True) -> str:
if model_size_b <= 7:
return "L40" if inference_only else "A100_PCIE_80GB"
elif model_size_b <= 13:
return "A100_PCIE_80GB"
elif model_size_b <= 70:
return "A100_PCIE_80GB (4x tensor parallel)"
else:
return "H100_SXM5 (8x tensor parallel)"
Quantization to Use Smaller GPUs
Use AWQ or GPTQ quantization to fit larger models on smaller GPUs:
# 70B model at 4-bit fits on single A100-80GB instead of 4x
vllm serve meta-llama/Llama-3.1-70B-Instruct-AWQ --quantization awq
Resources
Next Steps
For architecture patterns, see coreweave-reference-architecture.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.