Train In Silence
The first Task-Aware MCP server and automated VRAM calculator for LLM fine-tuning. Instantly snipe the cheapest, fastest GPUs across 10+ cloud providers.
What is Train In Silence?
Train In Silence is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to first task-aware mcp server and automated vram calculator for llm fine-tuning. instantly snipe the cheapest, fastest gpus across 10+ cloud providers.
The first Task-Aware MCP server and automated VRAM calculator for LLM fine-tuning. Instantly snipe the cheapest, fastest GPUs across 10+ cloud providers.
This server falls under the Cloud Services category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- The first Task-Aware MCP server and automated VRAM calculato
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx train-in-silenceConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Train In Silence
Train in Silence is a task-aware MCP server and automated VRAM/FLOPs calculator designed for LLM fine-tuning workflows. It analyzes your training code to estimate compute requirements, then queries 14+ GPU cloud providers (Vast.ai, RunPod, Lambda, and more) in real time to return ranked hardware recommendations — cheapest, fastest, or balanced. AI keys are optional; when omitted the server falls back to universal aggregators like GPUHunt. Developers use it to eliminate spreadsheet-based GPU shopping and instantly answer questions like "what is the cheapest setup to fine-tune this model in under 20 hours?"
Prerequisites
- Python 3.8 or later and pip installed
- An MCP client such as Claude Desktop or Claude Code
- Optional: Vast.ai API key (`VAST_API_KEY`) for live Vast.ai pricing
- Optional: RunPod API key (`RUNPOD_API_KEY`) for live RunPod pricing
- Training code or a YAML request file describing your fine-tuning task
Install Train in Silence
Install the package from PyPI. This installs both the `tis` CLI and the `tis-mcp` MCP server binary.
pip install train-in-silenceRegister as an MCP server in Claude Code (recommended)
Use the Claude Code CLI to register tis-mcp as a user-scoped MCP server. This makes it available in all Claude Code sessions.
claude mcp add tis --scope user -- tis-mcpAdd optional API keys for live pricing
Set API keys for cloud providers to get live pricing. If omitted, the server automatically falls back to universal aggregators with slightly less current data.
export VAST_API_KEY=your-vast-api-key
export RUNPOD_API_KEY=your-runpod-api-keyAdd to Claude Desktop (alternative to Claude Code)
If using Claude Desktop instead of Claude Code, add the server to `claude_desktop_config.json` with your optional API keys in the env block.
Ask Claude for GPU recommendations
Point Claude at your fine-tuning code directory or describe your task. The server automatically reads model parameters from your project files to generate accurate VRAM and compute estimates.
Train In Silence Examples
Client configuration
Add this to `claude_desktop_config.json` to use Train in Silence with optional provider API keys.
{
"mcpServers": {
"tis": {
"command": "tis-mcp",
"env": {
"VAST_API_KEY": "your-vast-api-key",
"RUNPOD_API_KEY": "your-runpod-api-key"
}
}
}
}Prompts to try
Use these prompts to get hardware recommendations and analyze your fine-tuning requirements.
- "Find the best GPU options across Vast.ai, RunPod, and Lambda to fine-tune this model within 20 hours."
- "What is the minimum VRAM needed to fine-tune the model in my current directory with QLoRA?"
- "List all available providers and their current status."
- "Show me the cheapest H100 options available right now across all providers."
- "Analyze the bottlenecks in my time estimate and suggest hardware trade-offs."Troubleshooting Train In Silence
tis-mcp command not found after installation
Ensure the Python scripts directory is on your PATH. On Linux/macOS, try `~/.local/bin/tis-mcp`. Alternatively, use `python -m tis.mcp` as the command in your MCP config.
Hardware recommendations only show sample data, not live pricing
Set `VAST_API_KEY` and/or `RUNPOD_API_KEY` environment variables to get live pricing. Without API keys, the server falls back to GPUHunt/GPUFinder aggregators, which may have older data.
VRAM estimates seem inaccurate for my model
Ensure your training script or config file is in the current working directory when prompting Claude. The `validate_request` tool can check your task description for completeness before calling `recommend_hardware`.
Frequently Asked Questions about Train In Silence
What is Train In Silence?
Train In Silence is a Model Context Protocol (MCP) server that first task-aware mcp server and automated vram calculator for llm fine-tuning. instantly snipe the cheapest, fastest gpus across 10+ cloud providers. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Train In Silence?
Follow the installation instructions on the Train In Silence GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Train In Silence?
Train In Silence works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Train In Silence free to use?
Yes, Train In Silence is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Set Up Train In Silence in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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