The Tool Desk
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What llmfit tells you about local models
llmfit is a hardware-aware recommendation tool, not a language model or a runtime for generating responses. The project says it detects CPU cores, system memory, GPUs and VRAM, accelerator details, and available runtime providers. It then evaluates catalog models using their parameter counts, context lengths and quantization formats, including GGUF, AWQ, GPTQ and EXL2. The model catalog is described as a curated Hugging Face catalog, with scores computed for the detected hardware profile.
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The project describes recommendations across four dimensions: memory fit, estimated speed, model quality and context. A high overall score is not automatically the best choice for every task. A model that fits comfortably may be preferable for a long context or responsive chat, while a different candidate may better suit a workload where model quality matters more than speed. Compare the underlying dimensions for your use rather than treating a single ranking as universal.
llmfit supports several local runtime providers, including Ollama, llama.cpp, MLX, Docker Model Runner and LM Studio. Its role is to help you assess options and feasible ways to run them; check the current project documentation for provider and platform compatibility. Official llmfit README · llmfit project site.
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How to choose a model that fits your computer
Start with your whole hardware profile
GPU memory is only part of the fit calculation. llmfit says it considers system RAM and CPU as well as GPU and VRAM, along with model configuration and context length. That matters because a model that does not fit entirely in VRAM might still have a feasible CPU-and-GPU or CPU-only run path, though that does not mean it will be fast enough for your needs.
Compare the trade-offs for your workload
For each candidate, look at memory fit, quantization, supported context, estimated speed and quality together. Consider what you actually plan to do—such as general chat or coding—and how much context the task needs. Quantization and execution mode affect the configuration being assessed, so compare like with like when weighing candidates. There is no single best local model independent of your hardware, workload and chosen settings.
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Check the suggested execution mode
The project documents recommendations for GPU, CPU plus GPU, CPU-only and MoE offload. Treat the mode as a proposed way to make a configuration feasible, not as a performance guarantee. The useful question is not just whether a model can run, but whether its memory needs and measured speed suit your task.
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Using llmfit to narrow and verify candidates
The project presents an interactive terminal interface as the default, alongside classic command-line options and a dashboard. In the TUI, you can search, filter, sort and compare models; its plan mode estimates memory requirements and feasible run paths. The README also documents JSON recommendations, model fit analysis, benchmark commands, a doctor report, and a web/API server. Exact commands and installation steps depend on the current release and platform, so use the README and project site for the latest instructions.
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- Inspect your hardware profile. Use llmfit’s detected CPU, RAM, GPU and VRAM details as the basis for filtering. Check that the detected accelerator and runtime availability reflect your actual setup.
- Filter and compare plausible models. Use the TUI’s search, filters and sorting to identify candidates, then compare memory fit, quality, context, quantization and estimated speed.
- Review the plan and run mode. Check the suggested memory needs and whether llmfit proposes GPU, CPU plus GPU, CPU-only or MoE offload for the configuration.
- Benchmark on the target computer. The README describes a workflow that downloads and serves a model, measures tokens per second on your hardware and saves the result locally. Use this measurement—not the estimate—as your evidence for expected performance on that machine.
How to interpret speed estimates
According to the README, llmfit’s estimates use a memory-bandwidth model informed by runtime sampling and community measurements. The project says llmfit info exposes assumptions and verification commands behind an estimate. Because the estimate is calculated rather than measured in your exact setup, treat it as a comparison aid. Runtime, model configuration and hardware can all affect actual results.
The documented benchmark workflow provides a way to measure real tokens per second on your computer, save results locally and optionally contribute a result through a project pull request. These are capabilities described by the project; recommendation accuracy and speed-estimate accuracy have not been independently established here. Benchmark the model and configuration you intend to use before relying on a speed figure.
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Platforms and installation options
The README lists macOS (Apple Silicon and Intel), Linux (x86_64 and ARM64), and Windows (x86_64), and calls out detection for NVIDIA CUDA, Apple Silicon, AMD ROCm and Intel OneAPI. It also lists installation routes including Windows Scoop, Homebrew, MacPorts, uv/pip, release binaries, Docker/Podman and building from source. Platform, accelerator, runtime and release support can change; verify the current documentation for your operating system and setup before installing or making a hardware decision.
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