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 Account - Sign up at https://app.primeintellect.ai
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

Automatic Setup: When you run pi setup, it:
- Connects to your clean Ubuntu pod
- Installs Python, CUDA drivers, and vLLM
- Configures HuggingFace tokens
- Sets up the model manager
Model Management: Each pi start command:
- Automatically finds an available GPU (on multi-GPU systems)
- Allocates the specified memory fraction
- Starts a separate vLLM instance on a unique port accessible via the OpenAI API protocol
- Manages logs and process lifecycle
Multi-GPU Support: On pods with multiple GPUs:
- Single models automatically distribute across available GPUs
- Large models can use tensor parallelism with --all-gpus
- 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

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
  --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

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

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 --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

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

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.
Parser Mismatches: Different models use different tool calling formats:
- Hermes format (XML-like)
- Mistral format (specific JSON structure)
- Llama format (JSON-based or pythonic)
- Custom formats for each model family
Common Issues:
- "Could not locate tool call start/end tokens" - Model doesn't have required special tokens
- Malformed JSON/XML output - Model wasn't trained for the parser format
- Tool calls when you don't want them - Model overeager to use tools
- 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

Let auto-detection handle it (default):

pi start meta-llama/Llama-3.1-8B-Instruct --name llama

Force a specific parser (if you know better):

pi start model/name --name mymodel --vllm-args \
  --tool-call-parser mistral --enable-auto-tool-choice

Disable tool calling entirely (most reliable):

pi start model/name --name mymodel --vllm-args \
  --disable-tool-call-parser

Handle tools in your application (recommended for production):
- Send regular prompts asking the model to output JSON
- Parse the response in your code
- More control, more reliable

Best Practices

Test first: Try a simple tool call to see if it works with your model
Have a fallback: Be prepared for tool calling to fail
Consider alternatives: Sometimes a well-crafted prompt works better than tool calling
Read the docs: Check the model card for tool calling examples
Monitor logs: Check ~/.vllm_logs/ for parser errors

Remember: Tool calling is still an evolving feature in the LLM ecosystem. What works today might break tomorrow with a model update.

Troubleshooting

OOM Errors: Reduce gpu_fraction or use a smaller model
Slow Inference: Could be too many concurrent requests, try increasing gpu_fraction
Connection Refused: Check pod is running and port is correct
HF Token Issues: Ensure HF_TOKEN is set before running setup
Access Denied: Some models (like Llama, Mistral) require completing an access request on HuggingFace first. Visit the model page and click "Request access"
Tool Calling Errors: See the Tool Calling section above - consider disabling it or using a different model

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Package last updated on 01 Aug 2025

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@badlogic/pi

GPU Pod Manager

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What This Is