llmfit: Find Which LLMs Actually Run on Your Hardware

What It Is

llmfit is a command-line tool that solves a frustrating problem: figuring out which large language models will actually run on available hardware before spending hours downloading them. The tool scans system specifications—RAM, CPU, and GPU configuration—then evaluates 497 models from 133 providers against those constraints. It produces compatibility scores based on quality, speed, fit, and context window size.

The tool automatically detects multi-GPU setups and calculates appropriate quantization levels. Instead of manually researching whether a 70-billion parameter model needs 4-bit quantization or can handle 8-bit precision, llmfit performs these calculations based on detected hardware. The result is a ranked list showing which models will load successfully and how they’ll perform.

Available at https://github.com/AlexsJones/llmfit, the tool runs locally and doesn’t require cloud services or API keys to generate recommendations.

Why It Matters

Model compatibility checking addresses a significant pain point in local LLM deployment. Developers and researchers frequently waste bandwidth and storage downloading models that exceed their hardware capabilities. A single 70B parameter model can consume 40-140GB depending on quantization, making trial-and-error approaches impractical.

Small teams and individual developers benefit most from this tool. Unlike organizations with dedicated ML infrastructure, these users often work with consumer hardware where memory constraints are tight. llmfit helps them identify the sweet spot between model capability and hardware limitations without requiring deep knowledge of quantization mathematics or memory requirements.

The tool also democratizes access to local LLM deployment. By removing the guesswork around hardware compatibility, it lowers the barrier for developers who want to run models locally for privacy, cost, or latency reasons but lack experience estimating resource requirements. This shifts the decision from “can I run local models?” to “which local models match my use case?”

For the broader ecosystem, tools like llmfit encourage more local deployment experimentation. When compatibility checking becomes trivial, developers are more likely to test multiple models and find optimal configurations rather than defaulting to cloud APIs.

Getting Started

Installation requires Python and pip:

Running the tool launches an interactive terminal interface:

The interface displays detected hardware specifications and presents ranked model recommendations. For users who prefer traditional command-line output over the interactive UI, llmfit includes flags to modify the display format.

The tool automatically scans system resources during startup. On multi-GPU systems, it detects all available devices and calculates aggregate memory. The scoring algorithm considers both total available memory and the distribution across devices when recommending models.

Results include specific quantization recommendations. For example, if a system has 24GB of VRAM, llmfit might recommend running Llama 2 70B at 4-bit quantization while suggesting Llama 2 13B could handle 8-bit quantization with room to spare.

Context

Several alternatives exist for estimating LLM hardware requirements. Manual calculation involves looking up model parameter counts and applying rough formulas (typically 2 bytes per parameter for 16-bit, 1 byte for 8-bit, 0.5 bytes for 4-bit). Online calculators like the LLM Memory Calculator provide web-based estimation, but require manual hardware input and don’t scan actual system configurations.

llmfit’s main limitation is its static model database. The tool evaluates 497 models, but new models release constantly. Users working with cutting-edge or niche models may need to fall back on manual estimation. The tool also focuses on inference requirements rather than fine-tuning, which demands significantly more memory.

Compared to framework-specific tools like llama.cpp’s memory estimation or vLLM’s profiling features, llmfit provides broader model coverage but less detailed performance prediction. It answers “will this run?” rather than “how many tokens per second will this generate?”

The tool works best as a first-pass filter. Developers can use llmfit to identify viable candidates, then benchmark specific models for production deployment decisions.