| name | coreweave-cost-tuning |
| description | Optimize CoreWeave GPU cloud costs with right-sizing and scheduling.
Use when reducing GPU spend, selecting cost-effective instances,
or implementing scale-to-zero for dev workloads.
Trigger with phrases like "coreweave cost", "coreweave pricing",
"reduce coreweave spend", "coreweave budget".
|
| allowed-tools | Read, Write, Edit, Bash(kubectl:*), Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","gpu-cloud","kubernetes","inference","coreweave"] |
| compatible-with | claude-code |
CoreWeave Cost Tuning
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:
vllm serve meta-llama/Llama-3.1-70B-Instruct-AWQ --quantization awq
