Runyard tools
built around local LLM decisions.

Find exactly how much VRAM any model needs at any quantization level and context length.

See how larger context windows change KV cache growth, memory pressure, and practical fit.

Choose the right quant level for your hardware, quality target, and inference speed goals.

Match your GPU directly to the best-fit models and skip dead-end downloads.

Estimate what your chosen Ollama context setting means for memory use and local stability.

Estimate likely local inference speed before you commit to a model and runtime stack.

A guided compatibility check that funnels users to Runyard home for the live answer.

Convert model parameter counts and quant formats into memory estimates you can reason about.

Estimate whether partial GPU offload will help or just create a slow, awkward compromise.

Pick the right GGUF file variant without memorizing every quant suffix and packaging nuance.

A recommendation-first landing page that points users back to Runyard home for the live shortlist.

Count prompt size, estimate context consumption, and catch oversized inputs before they fail.

Compare model families across fit, speed, context, and practical use-case tradeoffs.

Estimate how much local inference can save compared with repeated paid API usage.

Diagnose likely out-of-memory causes and suggest the fastest fixes for local inference failures.