Can it interact directly with my CoreWeave cluster?

Yes, using the Bash(kubectl) tool, Claude can view and modify your Kubernetes configurations to apply autoscaling policies and scheduling optimizations.

Does it support model quantization techniques?

Yes, it provides specific commands and logic for fitting larger models (like 70B parameters) into smaller VRAM footprints using AWQ and GPTQ quantization.

Which GPU instances are covered by this skill?

The skill includes pricing and optimization logic for A100 (40GB/80GB), H100 (PCIe and SXM5), and L40 instances, helping you choose the best performance-to-price ratio.

How does this skill help reduce CoreWeave costs?

It provides actionable guidance on right-sizing GPU instances, implementing scale-to-zero for idle workloads, and using quantization to run large models on significantly cheaper hardware.

CoreWeave Cost Optimizer

Name: CoreWeave Cost Optimizer
Author: jeremylongshore

byjeremylongshore

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2,028

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云基础设施

Optimizes CoreWeave GPU expenditures through intelligent instance selection, scheduling, and resource right-sizing.

This skill enables Claude to manage and minimize cloud costs on the CoreWeave platform by providing data-driven recommendations for GPU selection based on specific model requirements. It assists developers in implementing cost-saving measures such as Knative scale-to-zero configurations for development environments, applying quantization techniques to fit larger models on cheaper hardware, and identifying the most cost-effective instances—ranging from L40 to H100 SXM5—for diverse inference and training workloads.

主要功能

012,028 GitHub stars

02Quantization guidance to reduce hardware VRAM requirements

03Automated GPU right-sizing based on model parameter count

04Kubernetes-native resource management via kubectl integration

05Real-time pricing reference for CoreWeave GPU instances

06Scale-to-zero implementation for non-production environments

使用场景

01Reducing monthly cloud spend on high-concurrency inference servers

02Selecting the optimal GPU instance type for deploying Llama-3 or other large language models

03Configuring autoscaling for development and staging clusters to prevent idle costs

主要功能

012,028 GitHub stars

02Quantization guidance to reduce hardware VRAM requirements

03Automated GPU right-sizing based on model parameter count

04Kubernetes-native resource management via kubectl integration

05Real-time pricing reference for CoreWeave GPU instances

06Scale-to-zero implementation for non-production environments

使用场景

01Reducing monthly cloud spend on high-concurrency inference servers

02Selecting the optimal GPU instance type for deploying Llama-3 or other large language models

03Configuring autoscaling for development and staging clusters to prevent idle costs