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nodegrove VRAM: can I run it?

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    • chimeraforge

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      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.
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    • Train in Silence

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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A3.6/5.0

    Scored across 6 tools

    Disambiguation4/5

    Tools are separated along reasonable axes (model+GPU pair, model-only, GPU-only, arbitrary HF repo), but can_i_run, estimate_vram, estimate_from_hf_repo, and what_fits all return overlapping memory-fit information, so an agent must read descriptions carefully to pick the right entry point.

    Naming Consistency3/5

    Names use consistent snake_case, but conventions are mixed: list_* and estimate_* follow a verb_noun pattern while can_i_run and what_fits are question phrases. Still readable but not a single predictable scheme.

    Tool Count5/5

    Six tools is well-scoped for a VRAM-estimation server, with each tool covering a distinct query direction (catalog, forward estimate, reverse fit).

    Completeness4/5

    The surface covers model and GPU catalogs, both fit directions, and memory estimation for known and unknown models, forming a near-complete lifecycle. Minor gaps exist around exploring quantisation/context spaces without committing to a model, but most workflows are supported.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources