chimeraforge
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- AlicenseAqualityAmaintenanceChecks whether an open-weight LLM fits your GPU: VRAM for weights, KV cache and overhead at each quantisation, a tokens-per-second ceiling, the longest context that fits, and what would work instead when it doesn't. Reads any Hugging Face repo's config.json. Free, read-only, no API key.46MIT
- AlicenseAqualityAmaintenanceEvidence-backed private AI deployment intelligence for GPU and LLM inference. Search benchmark evidence, check deployment fit, predict performance, get hardware recommendations, and generate launch configurations.5GPL 3.0
- AlicenseAqualityDmaintenanceEstimates GPU requirements, training/inference costs, and cloud-vs-on-prem TCO for AI workloads using deterministic calculators.121MIT
- AlicenseNot gradedqualityCmaintenanceToken cost math for LLM API calls: current per-million-token rates for 69 models across 17 providers, with local arithmetic for estimates, comparisons and monthly budgets. Rates are verified and date-stamped.18 npm2MIT
- FlicenseNot gradedqualityAmaintenanceIt enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.-
- FlicenseNot gradedqualityAmaintenanceEnables users to describe an LLM fine-tuning job once and get the cheapest, fastest, or most balanced GPU options across multiple cloud providers by estimating required VRAM and FLOPs.-
TDQS
Scored across 5 tools
Each tool targets a distinct task: model metadata lookup (resolve_model), GPU metadata lookup (list_hardware), forward planning (plan), API-vs-self-host cost analysis (compare_api), and inverse ranking (suggest). The only potential overlap is plan vs suggest, but the description explicitly frames suggest as 'the inverse of planning,' resolving ambiguity.
All names share the consistent chimeraforge_ prefix and mostly use a verb_noun pattern (resolve_model, list_hardware, compare_api). Two entries are bare verbs (plan, suggest), a minor deviation that stays readable and predictable.
Five tools is well-scoped for a GPU/model deployment advisor: model resolution, hardware lookup, forward planning, cost comparison, and inverse suggestion each cover a distinct need. No tool feels redundant or missing at the structural level.
The surface covers the core advisory lifecycle: resolving models, listing hardware, planning deployments, comparing self-host vs API cost, and reverse-fitting models to GPUs. Minor gaps exist (e.g., no general catalog browsing or pricing-refresh beyond the dated snapshot noted in compare_api), but agents can work around them.