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9 tools
Check whether your GPU can run a given local LLM before downloading gigabytes. Compares your available VRAM against the model's requirements at each quantization level.
Calculate exactly how much GPU VRAM a local LLM needs at any quantization and context length. Enter a model and see its memory footprint before you download — no guesswork.
Find the cheapest GPU that can run the local LLM you want. Recommends hardware by required VRAM, quantization, and target context length.
Estimate and compare LLM API costs across providers by tokens, model, and request volume — and see the break-even point versus running the model on your own GPU.
Calculate the electricity cost of running a GPU for local AI inference. Enter wattage, hours, and your power rate to get the real cost per million tokens.
Pick the right GGUF quantization for your hardware. Compare Q4, Q5, Q6, Q8 and more on quality vs. VRAM so you fit the largest model your GPU can hold.
Work out how much VRAM a model's context window consumes and the maximum context length your GPU can handle for a given local LLM.
See which local LLM servers and gateways expose an OpenAI-compatible API, and which endpoints (chat, embeddings, tools) each one supports.
Generate ready-to-paste config for pointing Claude Code, Cursor, Aider, Cline, and other coding agents at your own local LLM via an OpenAI-compatible endpoint.