GPU Pod Manager

Quickly deploy LLMs on GPU pods from Prime Intellect, Vast.ai, DataCrunch, AWS, etc., for local coding agents and AI assistants.

Installation

npm install -g @mariozechner/pi

Or run directly with npx:

npx @mariozechner/pi

What This Is

A simple CLI tool that automatically sets up and manages vLLM deployments on GPU pods. Start from a clean Ubuntu pod and have multiple models running in minutes. A GPU pod is defined as an Ubuntu machine with root access, one or more GPUs, and Cuda drivers installed. It is aimed at individuals who are limited by local hardware and want to experiment with large open weight LLMs for their coding assistent workflows.

Key Features: - Zero to LLM in minutes - Automatically installs vLLM and all dependencies on clean pods - Multi-model management - Run multiple models concurrently on a single pod - Smart GPU allocation - Round robin assigns models to available GPUs on multi-GPU pods - Tensor parallelism - Run large models across multiple GPUs with --all-gpus - OpenAI-compatible API - Drop-in replacement for OpenAI API clients with automatic tool/function calling support - No complex setup - Just SSH access, no Kubernetes or Docker required - Privacy first - vLLM telemetry disabled by default

Limitations: - OpenAI endpoints exposed to the public internet (yolo) - Requires manual pod creation via Prime Intellect, Vast.ai, AWS, etc. - Assumes Ubuntu 22 image when creating pods

What this is not

• A provisioning manager for pods. You need to provision the pods on the respective provider themselves.
• Super optimized LLM deployment infrastructure for absolute best performance. This is for individuals who want to quickly spin up large open weights models for local LLM loads.

Requirements

• Node.js 14+ - To run the CLI tool on your machine
• HuggingFace Token - Required for downloading models (get one at https://huggingface.co/settings/tokens)
• Prime Intellect/DataCrunch/Vast.ai Account
• GPU Pod - At least one running pod with:
• Ubuntu 22+ image (selected when creating pod)
• SSH access enabled
• Clean state (no manual vLLM installation needed)
• Note: B200 GPUs require PyTorch nightly with CUDA 12.8+ (automatically installed if detected). However, vLLM may need to be built from source for full compatibility.

Quick Start

# 1. Get a GPU pod from Prime Intellect
#    Visit https://app.primeintellect.ai or https://vast.ai/ or https://datacrunch.io and create a pod (use Ubuntu 22+ image)
#    Providers usually give you an SSH command with which to log into the machine. Copy that command.

# 2. On your local machine, run the following to setup the remote pod. The Hugging Face token
#    is required for model download.
export HF_TOKEN=your_huggingface_token
pi setup my-pod-name "ssh root@135.181.71.41 -p 22"

# 3. Start a model (automatically manages GPU assignment)
pi start microsoft/Phi-3-mini-128k-instruct --name phi3 --memory 20%

# 4. Test the model with a prompt
pi prompt phi3 "What is 2+2?"
# Response: The answer is 4.

# 5. Start another model (automatically uses next available GPU on multi-GPU pods)
pi start Qwen/Qwen2.5-7B-Instruct --name qwen --memory 30%

# 6. Check running models
pi list

# 7. Use with your coding agent
export OPENAI_BASE_URL='http://135.181.71.41:8001/v1'  # For first model
export OPENAI_API_KEY='dummy'

How It Works

1. Automatic Setup: When you run pi setup, it:
2. Connects to your clean Ubuntu pod
3. Installs Python, CUDA drivers, and vLLM
4. Configures HuggingFace tokens

5. Sets up the model manager

6. Model Management: Each pi start command:

7. Automatically finds an available GPU (on multi-GPU systems)
8. Allocates the specified memory fraction
9. Starts a separate vLLM instance on a unique port accessible via the OpenAI API protocol

10. Manages logs and process lifecycle

11. Multi-GPU Support: On pods with multiple GPUs:

12. Single models automatically distribute across available GPUs
13. Large models can use tensor parallelism with --all-gpus
14. View GPU assignments with pi list

Commands

Pod Management

The tool supports managing multiple Prime Intellect pods from a single machine. Each pod is identified by a name you choose (e.g., "prod", "dev", "h200"). While all your pods continue running independently, the tool operates on one "active" pod at a time - all model commands (start, stop, list, etc.) are directed to this active pod. You can easily switch which pod is active to manage models on different machines.

pi setup <pod-name> "<ssh_command>"  # Configure and activate a pod
pi pods                              # List all pods (active pod marked)
pi pod <pod-name>                    # Switch active pod
pi pod remove <pod-name>             # Remove pod from config
pi shell                             # SSH into active pod

Working with Multiple Pods

You can manage models on any pod without switching the active pod by using the --pod parameter:

# List models on a specific pod
pi list --pod prod

# Start a model on a specific pod
pi start Qwen/Qwen2.5-7B-Instruct --name qwen --pod dev

# Stop a model on a specific pod
pi stop qwen --pod dev

# View logs from a specific pod
pi logs qwen --pod dev

# Test a model on a specific pod
pi prompt qwen "Hello!" --pod dev

# SSH into a specific pod
pi shell --pod prod
pi ssh --pod prod "nvidia-smi"

This allows you to manage multiple environments (dev, staging, production) from a single machine without constantly switching between them.

Model Management

Each model runs as a separate vLLM instance with its own port and GPU allocation. The tool automatically manages GPU assignment on multi-GPU systems and ensures models don't conflict. Models are accessed by their short names (either auto-generated or specified with --name).

pi list                              # List running models on active pod
pi search <query>                    # Search HuggingFace models
pi start <model> [options]           # Start a model with options
  --name <name>                      # Short alias (default: auto-generated)
  --context <size>                   # Context window: 4k, 8k, 16k, 32k (default: model default)
  --memory <percent>                 # GPU memory: 30%, 50%, 90% (default: 90%)
  --all-gpus                         # Use tensor parallelism across all GPUs
  --pod <pod-name>                   # Run on specific pod (default: active pod)
  --vllm-args                        # Pass all remaining args directly to vLLM
pi stop [name]                       # Stop a model (or all if no name)
pi logs <name>                       # View logs with tail -f
pi prompt <name> "message"           # Quick test prompt
pi downloads [--live]                # Check model download progress (--live for continuous monitoring)

All model management commands support the --pod parameter to target a specific pod without switching the active pod.

Examples

Search for models

pi search codellama
pi search deepseek
pi search qwen

Note: vLLM does not support formats like GGUF. Read the docs

A100 80GB scenarios

# Small model, high concurrency (~30-50 concurrent requests)
pi start microsoft/Phi-3-mini-128k-instruct --name phi3 --memory 30%

# Medium model, balanced (~10-20 concurrent requests)
pi start meta-llama/Llama-3.1-8B-Instruct --name llama8b --memory 50%

# Large model, limited concurrency (~5-10 concurrent requests)
pi start meta-llama/Llama-3.1-70B-Instruct --name llama70b --memory 90%

# Run multiple small models
pi start Qwen/Qwen2.5-Coder-1.5B --name coder1 --memory 15%
pi start microsoft/Phi-3-mini-128k-instruct --name phi3 --memory 15%

Understanding Context and Memory

Context Window vs Output Tokens

Models are loaded with their default context length. You can use the context parameter to specify a lower or higher context length. The context parameter sets the total token budget for input + output combined: - Starting a model with context=8k means 8,192 tokens total - If your prompt uses 6,000 tokens, you have 2,192 tokens left for the response - Each OpenAI API request to the model can specify max_output_tokens to control output length within this budget

Example:

# Start model with 32k total context
pi start meta-llama/Llama-3.1-8B --name llama --context 32k --memory 50%

# When calling the API, you control output length per request:
# - Send 20k token prompt
# - Request max_tokens=4000
# - Total = 24k (fits within 32k context)

GPU Memory and Concurrency

vLLM pre-allocates GPU memory controlled by gpu_fraction. This matters for coding agents that spawn sub-agents, as each connection needs memory.

Example: On an A100 80GB with a 7B model (FP16, ~14GB weights): - gpu_fraction=0.3 (24GB): ~10GB for KV cache → ~30-50 concurrent requests - gpu_fraction=0.5 (40GB): ~26GB for KV cache → ~50-80 concurrent requests - gpu_fraction=0.9 (72GB): ~58GB for KV cache → ~100+ concurrent requests

Models load in their native precision from HuggingFace (usually FP16/BF16). Check the model card's "Files and versions" tab - look for file sizes: 7B models are ~14GB, 13B are ~26GB, 70B are ~140GB. Quantized models (AWQ, GPTQ) in the name use less memory but may have quality trade-offs.

Multi-GPU Support

For pods with multiple GPUs, the tool automatically manages GPU assignment:

Automatic GPU assignment for multiple models

# Each model automatically uses the next available GPU
pi start microsoft/Phi-3-mini-128k-instruct --memory 20%  # Auto-assigns to GPU 0
pi start Qwen/Qwen2.5-7B-Instruct --memory 20%         # Auto-assigns to GPU 1
pi start meta-llama/Llama-3.1-8B --memory 20%          # Auto-assigns to GPU 2

# Check which GPU each model is using
pi list

Qwen on a single H200

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

Run large models across all GPUs

# Use --all-gpus for tensor parallelism across all available GPUs
pi start meta-llama/Llama-3.1-70B-Instruct --all-gpus
pi start Qwen/Qwen2.5-72B-Instruct --all-gpus --context 64k

Advanced: Custom vLLM arguments

# Pass custom arguments directly to vLLM with --vllm-args
# Everything after --vllm-args is passed to vLLM unchanged

# Qwen3-Coder 480B on 8xH200 with expert parallelism
pi start Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 --name qwen-coder --vllm-args \
  --data-parallel-size 8 --enable-expert-parallel \
  --tool-call-parser qwen3_coder --enable-auto-tool-choice --gpu-memory-utilization 0.95 --max-model-len 200000

# DeepSeek with custom quantization
pi start deepseek-ai/DeepSeek-Coder-V2-Instruct --name deepseek --vllm-args \
  --tensor-parallel-size 4 --quantization fp8 --trust-remote-code

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

Note on Special Models: Some models require specific vLLM arguments to run properly: - Qwen3-Coder 480B: Requires --enable-expert-parallel for MoE support - Kimi K2: May require custom arguments - check the model's documentation - DeepSeek V3: Often needs --trust-remote-code for custom architectures - When in doubt, consult the model's HuggingFace page or documentation for recommended vLLM settings

Check GPU usage

pi ssh "nvidia-smi"

Architecture Notes

• Multi-Pod Support: The tool stores multiple pod configurations in ~/.pi_config with one active pod at a time.
• Port Allocation: Each model runs on a separate port (8001, 8002, etc.) allowing multiple models on one GPU.
• Memory Management: vLLM uses PagedAttention for efficient memory use with less than 4% waste.
• Model Caching: Models are downloaded once and cached on the pod.
• Tool Parser Auto-Detection: The tool automatically selects the appropriate tool parser based on the model:
• Qwen models: hermes (Qwen3-Coder: qwen3_coder if available)
• Mistral models: mistral with optimized chat template
• Llama models: llama3_json or llama4_pythonic based on version
• InternLM models: internlm
• Phi models: Tool calling disabled by default (no compatible tokens)
• Override with --vllm-args --tool-call-parser <parser> --enable-auto-tool-choice

Tool Calling (Function Calling)

Tool calling allows LLMs to request the use of external functions/APIs, but it's a complex feature with many caveats:

The Reality of Tool Calling

1. Model Compatibility: Not all models support tool calling, even if they claim to. Many models lack the special tokens or training needed for reliable tool parsing.

2. Parser Mismatches: Different models use different tool calling formats:

3. Hermes format (XML-like)
4. Mistral format (specific JSON structure)
5. Llama format (JSON-based or pythonic)

6. Custom formats for each model family

7. Common Issues:

8. "Could not locate tool call start/end tokens" - Model doesn't have required special tokens
9. Malformed JSON/XML output - Model wasn't trained for the parser format
10. Tool calls when you don't want them - Model overeager to use tools
11. No tool calls when you need them - Model doesn't understand when to use tools

How We Handle It

The tool automatically detects the model and tries to use an appropriate parser: - Qwen models: hermes parser (Qwen3-Coder uses qwen3_coder) - Mistral models: mistral parser with custom template - Llama models: llama3_json or llama4_pythonic based on version - Phi models: Tool calling disabled (no compatible tokens)

Your Options

1. Let auto-detection handle it (default): ```bash pi star