coreweave-cost-tuning

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 <[e-mail]>
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