gpu-router
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Alternatives to gpu-router
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- AlicenseCqualityBmaintenanceA local stdio MCP server that lets agents submit and manage Python/notebook jobs on Kaggle and Google Colab, with quota-aware provider recommendation, job listing/reconciliation, downloads, and cancellation. It runs in a safe mock mode by default and requires double-gated flags before any real GPU or CCU-billed work is submitted.302Apache 2.0
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to manage GPU training end-to-end through natural language, including submitting and scheduling jobs, monitoring logs and metrics, diagnosing failures, comparing runs, and recommending the best checkpoints.Apache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to autonomously manage Google Colab GPU sessions, submit and monitor training jobs, and debug/fix issues via an encrypted tunnel without requiring a browser tab.MIT
- AlicenseBqualityCmaintenanceEnables AI agents to plan, confirm, submit, observe, and audit compute jobs across local machines, SSH workstations (including WSL), and Slurm clusters through a unified stdio interface, with node probing, environment evidence, project sync, storage governance, and progress/ETA tracking.311MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to orchestrate multi-system LLM training, quantization, and evaluation workloads across local, Colab, Kaggle, and SSH targets, with persistent monitoring, historical analysis, and visualization via MCP tools.MIT
- AlicenseAqualityBmaintenanceYour AI agents won't set your GPU on fire. GPU/CPU/RAM scheduler + market maker for AI coding agents (Claude Code, Codex, Gemini, Cursor, Grok, OpenCode, Kimi, Copilot and more) sharing one PC. Any mix of GPUs, MCP server, one-command install, Windows/macOS/Linux.5MIT
TDQS
Scored across 8 tools
Each tool targets a distinct verb on the job lifecycle (submit, route, cancel, status, logs, fetch) or a separate resource (quota, infer). The only near-pair is gpu_route vs gpu_submit, but the descriptions make the dry-run vs actual-submit distinction explicit. No meaningful overlap remains.
Every tool uses the same gpu_<verb> snake_case pattern (gpu_cancel, gpu_quota, gpu_route, gpu_infer, gpu_submit, gpu_status, gpu_logs, gpu_fetch). Verb choices are predictable and readable. Fully consistent convention throughout.
Eight tools is well within the ideal 3-15 range for the domain. Each tool earns its place by owning a distinct operation (submit, route, cancel, status, logs, fetch, quota, infer). No redundancy or padding.
The job lifecycle is well covered: dry-run routing, submission, cancellation, status/monitoring, logs, and output retrieval, plus quota and inference. Minor gaps exist (e.g. no explicit resume/retry of a failed job or a dedicated list-jobs tool, though gpu_status covers the overview). These are workable-around, not blocking.