Will a local LLM run on your hardware? GGUF quant, buy-vs-rent-vs-API cost, used-GPU prices.
Scored across 9 tools
Several tools address hardware/model fit: can_i_run_it, recommend_hardware, cheapest_hardware_for_model, and compare_hardware all involve fit or tok/s, so an agent may struggle to pick the right one. That said, the descriptions carve out distinct intents (single-machine check, ranked purchase list, cheapest machine, side-by-side comparison), making the overlap manageable.
Most tools use a clear verb_noun form (list_hardware, compare_hardware, recommend_quant), but can_i_run_it is a question, cheapest_hardware_for_model is a descriptive phrase, and cost_compare flips the verb order. The pattern is readable but not uniform.
Nine tools is well-scoped for a hardware/model advisory service; each tool addresses a distinct decision step from compatibility to cost to comparison. No tool feels redundant enough to cut.
The set covers compatibility checking, hardware/model catalogs, purchase recommendations, quant selection, cost modeling, and used-GPU pricing, which is comprehensive for the domain. There are no obvious dead ends for a consumer evaluating local-LLM hardware.
Will a given local LLM run on given hardware? Returns fit, the best quant that fits, theoretical tok/s, and real owner-measured tok/s where available.
Inputs: model, mxfp4, context, total_b, +6 more.
The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.
Inputs: model, mxfp4, context, total_b, +1 more.
Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.
Inputs: hardware (required), model, mxfp4, context, +3 more.
Buy vs rent vs API cost to run a model locally: monthly/1y/3y totals, break-even months, and the energy cost per 1M tokens. Same math as /cost-calculator/.
Inputs: api, kwh, rent, hours, +4 more.
Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
Inputs: gpu.
List the machines the tools know about (memory, bandwidth, price, buy link).
Inputs: no named parameters.
List the local LLM model classes the tools know about (params, dense/MoE, native context).
Inputs: no named parameters.
Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.
Inputs: model, mxfp4, budget, context, +3 more.
Which GGUF quantization to download for a model on given hardware: the full quant ladder with file size, max context, and tok/s for each, plus the recommended pick.
Inputs: model, mxfp4, context, total_b, +6 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Can this LLM run on my GPU? VRAM, speed ceiling and what fits instead, for any model and GPU.
Does an LLM fit on your GPU? VRAM, KV cache and GPU count for any Hugging Face model.
Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.
Which open models fit your GPU or Mac, measured. Model momentum, GPU rental prices, weekly pick.
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