Coreweave Cost Tuning

DevOps & Security

coreweave-cost-tuning

Coreweave Cost Tuning is an Agent Skill for devops & security published in the jeremylongshore/claude-code-plugins-plus-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

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".

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QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

Open your project in Codex.
Copy and paste the prompt below.
Review files and risks before approving any action.

Prompt to paste

Inspect the Agent Skill at https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/plugins/saas-packs/coreweave-pack/skills/coreweave-cost-tuning/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/coreweave-cost-tuning, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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.