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Alternatives to gpu-router

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    Related Servers

    • A
      license
      C
      quality
      B
      maintenance
      A 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.
      30
      2
      Apache 2.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables 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
    • A
      license
      B
      quality
      C
      maintenance
      Enables 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.
      31
      1
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Your 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.
      5
      MIT

    TDQS

    A4.5/5.0

    Scored across 8 tools

    Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityMaintained
    ResponsivenessOutdated data