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Related Servers

Alternatives to ComputeSage server

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      A
      maintenance
      LLM deployment planner: given a model and a GPU, answers will it fit, will it hit your SLO, and what will it cost. Sizes VRAM and KV-cache from the model's real architecture, and labels every number measured, estimated, or unknown.
      5
      525 PyPI
      2
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to pull measured local-LLM benchmark data and hardware-aware model picks — ranked configs with decode tok/s, quant and backend details, hardware catalogues, and repo submissions for review. Answers questions like "what should I run on a Strix Halo for coding?" with sourced numbers rather than invented ones.
      129 npm
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Exposes queryable GPU inference benchmark data (quantization, throughput, VRAM, concurrent users) as tools for LLM clients.
      MIT

    TDQS

    A3.7/5.0

    Scored across 5 tools

    Disambiguation4/5

    Each tool targets a distinct stage in the evidence-to-deployment workflow. check_deployment_fit and predict_performance are related but clearly separated by fit versus metrics, and recommend_deployment is the only economic recommendation tool.

    Naming Consistency5/5

    All five tools use consistent lowercase snake_case verb_noun names with concrete action verbs (search, check, predict, recommend, generate), making the naming pattern predictable and readable.

    Tool Count5/5

    Five tools is appropriate for a specialized advice-and-configuration server. Every tool serves a distinct step in the workflow without redundancy or unnecessary surface area.

    Completeness5/5

    The server covers the full advisory pipeline: evidence search, fit validation, performance prediction, recommendation, and launch configuration generation. It intentionally stops before executing deployment, which is consistent with its stated purpose.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues