llmfit
An open-source command-line tool written in Rust for determining which large language models a local hardware configuration can run. It supports evaluation across hundreds of model and inference-backend combinations, helping developers assess hardware compatibility before downloading models.
Timeline (last 90 days)
llmfit (abbreviated as LMF8) is a tool that helps users first screen local large model candidates and then determine the best fit by testing with their own tasks.
This article is based on version 1.1.15 of llmfit.
The core logic of llmfit is to estimate models, quantization, and execution methods together in a single table, built on the foundation of correctly reading hardware.
llmfit can be installed on Windows via Scoop, and on Mac and Linux via the official Homebrew formula.
In llmfit, selecting a model and pressing lowercase P enters planning mode, while uppercase S allows temporarily simulating different memory capacities.
Reddit users in March felt that llmfit's speed estimates were too optimistic, and in July the author acknowledged in the 1.0 announcement that the estimates were insufficiently accurate, with local testing and community data now incorporated.
Speed figures in llmfit come from three sources: locally benchmarked results, results measured by others under similar conditions, and purely formula-based estimates.
In llmfit, selecting a model and pressing lowercase D downloads it, and pressing lowercase B can start a benchmark; the official example performs three rounds of inference to measure generation speed and time to first token.
The measure of whether a local model is useful should be how long it takes to complete a task correctly, not merely how many tokens per second it outputs; a model that outputs tokens quickly but repeatedly makes and corrects errors is not necessarily more time-efficient.
All Facts (8)
llmfit (abbreviated as LMF8) is a tool that helps users first screen local large model candidates and then determine the best fit by testing with their own tasks.
50%UnverifiedThe core logic of llmfit is to estimate models, quantization, and execution methods together in a single table, built on the foundation of correctly reading hardware.
50%Unverifiedllmfit can be installed on Windows via Scoop, and on Mac and Linux via the official Homebrew formula.
50%UnverifiedIn llmfit, selecting a model and pressing lowercase P enters planning mode, while uppercase S allows temporarily simulating different memory capacities.
50%UnverifiedThe measure of whether a local model is useful should be how long it takes to complete a task correctly, not merely how many tokens per second it outputs; a model that outputs tokens quickly but repeatedly makes and corrects errors is not necessarily more time-efficient.
50%UnverifiedSpeed figures in llmfit come from three sources: locally benchmarked results, results measured by others under similar conditions, and purely formula-based estimates.
50%UnverifiedIn llmfit, selecting a model and pressing lowercase D downloads it, and pressing lowercase B can start a benchmark; the official example performs three rounds of inference to measure generation speed and time to first token.
50%UnverifiedReddit users in March felt that llmfit's speed estimates were too optimistic, and in July the author acknowledged in the 1.0 announcement that the estimates were insufficiently accurate, with local testing and community data now incorporated.
50%