by hlpun · MCP Server · ★ 102
Train in Silence The first Task-Aware MCP server for LLM fine-tuning. Stop comparing GPU prices. Start training. 中文 You want to fine-tune an LLM. You open Vast.ai, RunPod, AWS, etc. -- a dozen tabs, a dozen pricing models, a dozen different ways to describe a GPU. Which option can run your code, and do so more cheaply and quickly? An hour later you're still in a spreadsheet and haven't written a single line of training code. Train in Silence is the first Task-Aware MCP server for LLM fine-tuning. It doesn't just list prices; it understands your workload.
| Stars | 102 |
| Forks | 2 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.1929645826075/100 |
| Last Updated | 2026-06-24 |
| Created | 2026-04-12 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~14k |
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Train-in-Silence is The first Task-Aware MCP server and automated VRAM calculator for LLM fine-tuning. Instantly snipe the cheapest, fastest GPUs across 10+ cloud providers.. It is categorized as a MCP Server with 102 GitHub stars.
Train-in-Silence is primarily written in Python. It covers topics such as claude-code, cost-optimization, fine-tuning.
You can find installation instructions and usage details in the Train-in-Silence GitHub repository at github.com/hlpun/Train-in-Silence. The project has 102 stars and 2 forks, indicating an active community.
Train-in-Silence is released under the MIT license, making it free to use and modify according to the license terms.