AI Agent Workflows

GPU CLI works well with coding agents that share your local workspace. The practical workflow today is:

1. install and authenticate GPU CLI locally
2. let the agent inspect your repo and generate or edit gpu.jsonc
3. use gpu agent-docs when the agent needs machine-oriented command and workflow reference
4. run gpu run, gpu llm run, gpu use, or gpu serverless from the same repo

Start with gpu agent-docs

gpu agent-docs prints agent-focused reference material to stdout.

gpu agent-docs
gpu agent-docs | head -50

Use it when your agent needs a compact source of truth for:

• command names and examples
• workflow shape
• JSON output behavior
• common troubleshooting hints

Good Agent Tasks

Agents are a good fit for:

• generating gpu.jsonc from a project description
• choosing GPU types and output patterns
• setting up gpu llm and template workflows
• debugging sync, auth, and startup issues
• adding provider or model-hub auth instructions to project docs

Common Workflows

Generate a project config

Create a gpu.jsonc for fine-tuning on an RTX 4090, sync checkpoints/, and keep the pod warm for 20 minutes.

Set up a routed LLM pod

Configure this repo for gpu llm run with vLLM and a HuggingFace model.

Turn a project into a reusable template

Create a gpu.jsonc template with startup, ports, and hooks so I can run it with gpu use.

Built-In GPU CLI Paths to Know

• gpu run — general remote command execution
• gpu llm run — routed Ollama or vLLM workflow
• gpu use ollama / gpu use vllm — official LLM templates
• gpu comfyui ... — curated ComfyUI workflows
• gpu serverless ... — RunPod Serverless deployment

Official Templates Mentioned Often by Agents

• hello-gpu
• ollama
• vllm
• invokeai
• crewai
• image-to-3d
• qwen
• chatterbox

Requirements

• GPU CLI installed and authenticated
• a coding agent that can read and edit the same local workspace you use
• provider credentials configured with gpu auth login

Related Docs

• LLM Inference
• Configuration
• Commands Reference
• Troubleshooting