pi

Deploy and manage LLMs on GPU pods with automatic vLLM configuration for agentic workloads.

Installation

``bash
npm install -g @mariozechner/pi
`

What is pi?

pi simplifies running large language models on remote GPU pods. It automatically:
- Sets up vLLM on fresh Ubuntu pods
- Configures tool calling for agentic models (Qwen, GPT-OSS, GLM, etc.)
- Manages multiple models on the same pod with "smart" GPU allocation
- Provides OpenAI-compatible API endpoints for each model
- Includes an interactive agent with file system tools for testing

Quick Start

`bash


Set required environment variables

export HF_TOKEN=your_huggingface_token # Get from https://huggingface.co/settings/tokens
export PI_API_KEY=your_api_key # Any string you want for API authentication

Setup a DataCrunch pod with NFS storage (models path auto-extracted)

pi pods setup dc1 "ssh root@1.2.3.4" \
--mount "sudo mount -t nfs -o nconnect=16 nfs.fin-02.datacrunch.io:/your-pseudo /mnt/hf-models"

Start a model (automatic configuration for known models)

pi start Qwen/Qwen2.5-Coder-32B-Instruct --name qwen

Send a single message to the model

pi agent qwen "What is the Fibonacci sequence?"

Interactive chat mode with file system tools

pi agent qwen -i

Use with any OpenAI-compatible client

export OPENAI_BASE_URL='http://1.2.3.4:8001/v1'
export OPENAI_API_KEY=$PI_API_KEY
`

Prerequisites

- Node.js 18+
- HuggingFace token (for model downloads)
- GPU pod with:
- Ubuntu 22.04 or 24.04
- SSH root access
- NVIDIA drivers installed
- Persistent storage for models

Supported Providers

$3

DataCrunch - Best for shared model storage
- NFS volumes sharable across multiple pods in same region
- Models download once, use everywhere
- Ideal for teams or multiple experiments

RunPod - Good persistent storage
- Network volumes persist independently
- Cannot share between running pods simultaneously
- Good for single-pod workflows

$3

- Vast.ai (volumes locked to specific machine)
- Prime Intellect (no persistent storage)
- AWS EC2 (with EFS setup)
- Any Ubuntu machine with NVIDIA GPUs, CUDA driver, and SSH

Commands

$3

`bash
pi pods setup "" [options] # Setup new pod
--mount "" # Run mount command during setup
--models-path # Override extracted path (optional)
--vllm release|nightly|gpt-oss # vLLM version (default: release)

pi pods # List all configured pods
pi pods active # Switch active pod
pi pods remove # Remove pod from local config
pi shell [] # SSH into pod
pi ssh [] "" # Run command on pod
`

Note: When using --mount, the models path is automatically extracted from the mount command's target directory. You only need --models-path if not using --mount or to override the extracted path.

#### vLLM Version Options

- release (default): Stable vLLM release, recommended for most users
- nightly: Latest vLLM features, needed for newest models like GLM-4.5
- gpt-oss: Special build for OpenAI's GPT-OSS models only

$3

`bash
pi start --name [options] # Start a model
--memory # GPU memory: 30%, 50%, 90% (default: 90%)
--context # Context window: 4k, 8k, 16k, 32k, 64k, 128k
--gpus # Number of GPUs to use (predefined models only)
--pod # Target specific pod (overrides active)
--vllm # Pass custom args directly to vLLM

pi stop [] # Stop model (or all if no name given)
pi list # List running models with status
pi logs # Stream model logs (tail -f)
`

$3

`bash
pi agent "" # Single message to model
pi agent "" "" # Multiple messages in sequence
pi agent -i # Interactive chat mode
pi agent -i -c # Continue previous session

Standalone OpenAI-compatible agent (works with any API)


pi-agent --base-url http://localhost:8000/v1 --model llama-3.1 "Hello"
pi-agent --api-key sk-... "What is 2+2?" # Uses OpenAI by default
pi-agent --json "What is 2+2?" # Output event stream as JSONL
pi-agent -i # Interactive mode
`

The agent includes tools for file operations (read, list, bash, glob, rg) to test agentic capabilities, particularly useful for code navigation and analysis tasks.

Predefined Model Configurations

pi includes predefined configurations for popular agentic models, so you do not have to specify --vllm arguments manually. pi will also check if the model you selected can actually run on your pod with respect to the number of GPUs and available VRAM. Run pi start without additional arguments to see a list of predefined models that can run on the active pod.

$3


`bash

Qwen2.5-Coder-32B - Excellent coding model, fits on single H100/H200

pi start Qwen/Qwen2.5-Coder-32B-Instruct --name qwen

Qwen3-Coder-30B - Advanced reasoning with tool use

pi start Qwen/Qwen3-Coder-30B-A3B-Instruct --name qwen3

Qwen3-Coder-480B - State-of-the-art on 8xH200 (data-parallel mode)

pi start Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 --name qwen-480b
`

$3

`bash

Requires special vLLM build during setup

pi pods setup gpt-pod "ssh root@1.2.3.4" --models-path /workspace --vllm gpt-oss

GPT-OSS-20B - Fits on 16GB+ VRAM

pi start openai/gpt-oss-20b --name gpt20

GPT-OSS-120B - Needs 60GB+ VRAM

pi start openai/gpt-oss-120b --name gpt120
`

$3

`bash

GLM-4.5 - Requires 8-16 GPUs, includes thinking mode

pi start zai-org/GLM-4.5 --name glm

GLM-4.5-Air - Smaller version, 1-2 GPUs

pi start zai-org/GLM-4.5-Air --name glm-air
`

$3

For models not in the predefined list, use --vllm to pass arguments directly to vLLM:

`bash


DeepSeek with custom settings

pi start deepseek-ai/DeepSeek-V3 --name deepseek --vllm \
--tensor-parallel-size 4 --trust-remote-code

Mistral with pipeline parallelism

pi start mistralai/Mixtral-8x22B-Instruct-v0.1 --name mixtral --vllm \
--tensor-parallel-size 8 --pipeline-parallel-size 2

Any model with specific tool parser

pi start some/model --name mymodel --vllm \
--tool-call-parser hermes --enable-auto-tool-choice
`

DataCrunch Setup

DataCrunch offers the best experience with shared NFS storage across pods:

$3

- Go to DataCrunch dashboard → Storage → Create SFS
- Choose size and datacenter
- Note the mount command (e.g., sudo mount -t nfs -o nconnect=16 nfs.fin-02.datacrunch.io:/hf-models-fin02-8ac1bab7 /mnt/hf-models-fin02)

$3

- Create instance in same datacenter as SFS
- Share the SFS with the instance
- Get SSH command from dashboard

$3

`bash

Get mount command from DataCrunch dashboard

pi pods setup dc1 "ssh root@instance.datacrunch.io" \
--mount "sudo mount -t nfs -o nconnect=16 nfs.fin-02.datacrunch.io:/your-pseudo /mnt/hf-models"

Models automatically stored in /mnt/hf-models (extracted from mount command)

`

$3

- Models persist across instance restarts
- Share models between multiple instances in same datacenter
- Download once, use everywhere
- Pay only for storage, not compute time during downloads

RunPod Setup

RunPod offers good persistent storage with network volumes:

$3

- Go to RunPod dashboard → Storage → Create Network Volume
- Choose size and region

$3

- Select "Network Volume" during pod creation (if using)
- Attach your volume to /runpod-volume
- Get SSH command from pod details

$3

`bash

With network volume

pi pods setup runpod "ssh root@pod.runpod.io" --models-path /runpod-volume

Or use workspace (persists with pod but not shareable)

pi pods setup runpod "ssh root@pod.runpod.io" --models-path /workspace
`

Multi-GPU Support

$3

When running multiple models, pi automatically assigns them to different GPUs:
`bash
pi start model1 --name m1 # Auto-assigns to GPU 0
pi start model2 --name m2 # Auto-assigns to GPU 1
pi start model3 --name m3 # Auto-assigns to GPU 2
`

$3

For predefined models with multiple configurations, use --gpus to control GPU usage:
`bash

Run Qwen on 1 GPU instead of all available

pi start Qwen/Qwen2.5-Coder-32B-Instruct --name qwen --gpus 1

Run GLM-4.5 on 8 GPUs (if it has an 8-GPU config)

pi start zai-org/GLM-4.5 --name glm --gpus 8
`

If the model doesn't have a configuration for the requested GPU count, you'll see available options.

$3

For models that don't fit on a single GPU:
`bash

Use all available GPUs

pi start meta-llama/Llama-3.1-70B-Instruct --name llama70b --vllm \
--tensor-parallel-size 4

Specific GPU count

pi start Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 --name qwen480 --vllm \
--data-parallel-size 8 --enable-expert-parallel
`

API Integration

All models expose OpenAI-compatible endpoints:

`python
from openai import OpenAI

client = OpenAI(
base_url="http://your-pod-ip:8001/v1",
api_key="your-pi-api-key"
)

Chat completion with tool calling


response = client.chat.completions.create(
model="Qwen/Qwen2.5-Coder-32B-Instruct",
messages=[
{"role": "user", "content": "Write a Python function to calculate fibonacci"}
],
tools=[{
"type": "function",
"function": {
"name": "execute_code",
"description": "Execute Python code",
"parameters": {
"type": "object",
"properties": {
"code": {"type": "string"}
},
"required": ["code"]
}
}
}],
tool_choice="auto"
)
`

Standalone Agent CLI

pi includes a standalone OpenAI-compatible agent that can work with any API:

`bash


Install globally to get pi-agent command

npm install -g @mariozechner/pi

Use with OpenAI

pi-agent --api-key sk-... "What is machine learning?"

Use with local vLLM

pi-agent --base-url http://localhost:8000/v1 \
--model meta-llama/Llama-3.1-8B-Instruct \
--api-key dummy \
"Explain quantum computing"

Interactive mode

pi-agent -i

Continue previous session

pi-agent --continue "Follow up question"

Custom system prompt

pi-agent --system-prompt "You are a Python expert" "Write a web scraper"

Use responses API (for GPT-OSS models)

pi-agent --api responses --model openai/gpt-oss-20b "Hello"
`

The agent supports:
- Session persistence across conversations
- Interactive TUI mode with syntax highlighting
- File system tools (read, list, bash, glob, rg) for code navigation
- Both Chat Completions and Responses API formats
- Custom system prompts

Tool Calling Support

pi automatically configures appropriate tool calling parsers for known models:

- Qwen models: hermes parser (Qwen3-Coder uses qwen3_coder)
- GLM models: glm4_moe parser with reasoning support
- GPT-OSS models: Uses /v1/responses endpoint, as tool calling (function calling in OpenAI parlance) is currently a WIP with the v1/chat/completions endpoint.
- Custom models: Specify with --vllm --tool-call-parser --enable-auto-tool-choice

To disable tool calling:
`bash
pi start model --name mymodel --vllm --disable-tool-call-parser
`

Memory and Context Management

$3


Controls how much GPU memory vLLM pre-allocates:
- --memory 30%: High concurrency, limited context
- --memory 50%: Balanced (default)
- --memory 90%: Maximum context, low concurrency

$3

Sets maximum input + output tokens:
- --context 4k: 4,096 tokens total
- --context 32k: 32,768 tokens total
- --context 128k: 131,072 tokens total

Example for coding workload:
`bash


Large context for code analysis, moderate concurrency


pi start Qwen/Qwen2.5-Coder-32B-Instruct --name coder \
--context 64k --memory 70%
`

Note: When using --vllm, the --memory, --context, and --gpus parameters are ignored. You'll see a warning if you try to use them together.

Session Persistence

The interactive agent mode (-i) saves sessions for each project directory:

`bash


Start new session

pi agent qwen -i

Continue previous session (maintains chat history)

pi agent qwen -i -c
`

Sessions are stored in ~/.pi/sessions/ organized by project path and include:
- Complete conversation history
- Tool call results
- Token usage statistics

Architecture & Event System

The agent uses a unified event-based architecture where all interactions flow through AgentEvent types. This enables:
- Consistent UI rendering across console and TUI modes
- Session recording and replay
- Clean separation between API calls and UI updates
- JSON output mode for programmatic integration

Events are automatically converted to the appropriate API format (Chat Completions or Responses) based on the model type.

$3

Use --json flag to output the event stream as JSONL (JSON Lines) for programmatic consumption:
`bash
pi-agent --api-key sk-... --json "What is 2+2?"
`

Each line is a complete JSON object representing an event:
`jsonl
{"type":"user_message","text":"What is 2+2?"}
{"type":"assistant_start"}
{"type":"assistant_message","text":"2 + 2 = 4"}
{"type":"token_usage","inputTokens":10,"outputTokens":5,"totalTokens":15,"cacheReadTokens":0,"cacheWriteTokens":0}
`

Troubleshooting

$3


- Reduce --memory percentage
- Use smaller model or quantized version (FP8)
- Reduce --context size

$3

`bash

Check GPU usage

pi ssh "nvidia-smi"

Check if port is in use

pi list

Force stop all models

pi stop
`

$3

- Not all models support tool calling reliably
- Try different parser: --vllm --tool-call-parser mistral
- Or disable: --vllm --disable-tool-call-parser

$3

Some models (Llama, Mistral) require HuggingFace access approval. Visit the model page and click "Request access".

$3

If using --vllm nightly fails, try:
- Use --vllm release for stable version
- Check CUDA compatibility with pi ssh "nvidia-smi"

$3

If the agent shows configuration instead of your message, ensure quotes around messages with special characters:
`bash

Good

pi agent qwen "What is this file about?"

Bad (shell might interpret special chars)

pi agent qwen What is this file about?
`

Advanced Usage

$3

`bash

Override active pod for any command

pi start model --name test --pod dev-pod
pi list --pod prod-pod
pi stop test --pod dev-pod
`

$3

`bash

Pass any vLLM argument after --vllm

pi start model --name custom --vllm \
--quantization awq \
--enable-prefix-caching \
--max-num-seqs 256 \
--gpu-memory-utilization 0.95
`

$3

`bash

Watch GPU utilization

pi ssh "watch -n 1 nvidia-smi"

Check model downloads

pi ssh "du -sh ~/.cache/huggingface/hub/*"

View all logs

pi ssh "ls -la ~/.vllm_logs/"

Check agent session history

ls -la ~/.pi/sessions/
`

Environment Variables

- HF_TOKEN - HuggingFace token for model downloads
- PI_API_KEY - API key for vLLM endpoints
- PI_CONFIG_DIR - Config directory (default: ~/.pi)
- OPENAI_API_KEY - Used by pi-agent when no --api-key` provided

License

MIT