Recommended Free Tools
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llmfit helps you shortlist open-source language models that may run on your existing computer by checking its CPU, system RAM, GPU and VRAM, then weighing model fit, estimated speed, quality, and context. Use its recommendations to narrow the field—not as a guarantee of real-world speed. Benchmark promising candidates on your own hardware before relying on them.
What llmfit checks—and what it recommends
llmfit is a hardware-aware model selection tool, not a language model or a runtime. It detects hardware and available runtime providers, then compares that profile with model metadata and quantization options. Its analysis includes CPU cores, system memory, GPU memory, accelerator details, model size, context length, and formats such as GGUF, AWQ, GPTQ, and EXL2. The project describes its catalog as a curated Hugging Face collection, with scores calculated for the detected hardware profile. See the llmfit README and project site for the current feature set.
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GMKtec EVO-X2 AI Mini PC Ryzen Al Max+ 395 Superchip 128GB LPDDR5X 2TB SSD | $3,649.99 | Buy on Amazon |
The result is a shortlist and a suggested way to run a model. Depending on the model and machine, llmfit may suggest GPU execution, CPU and GPU together, CPU-only execution, or MoE offload. It supports providers including Ollama, llama.cpp, MLX, Docker Model Runner, and LM Studio; verify current integrations and availability in the project documentation.
How to choose among the recommended models
There is no universally best local model: a candidate that suits general chat may not be the right choice for coding, and the largest model that fits is not automatically the most useful. Compare the dimensions that matter for your task rather than treating one overall score as a final verdict.
#1 Best Overall
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- Memory fit: Consider system RAM and GPU/VRAM together. A model’s practical fit also depends on its quantization and how much context you intend to use.
- Execution mode: Check whether the suggested path uses the GPU, splits work between CPU and GPU, runs on CPU, or uses MoE offload. A feasible path is not necessarily the fastest or most convenient for your setup.
- Estimated speed: Treat this as a screening estimate, not a measured promise. Actual performance depends on your machine and runtime.
- Quality and context: Weigh the model’s quality and supported context against the workload. A longer supported context can be useful only if it fits your actual needs and resource budget.
- Quantization: Compare the suggested format and precision as part of the fit and quality trade-off; do not compare model names alone.
Use the TUI’s search, filters, sorting, comparison, and plan mode to narrow candidates and inspect estimated memory requirements and feasible run paths. The project’s classic CLI and dashboard offer other ways to review recommendations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify estimates with a benchmark on your computer
The README says llmfit’s speed estimates use a memory-bandwidth model informed by runtime sampling and community measurements. The llmfit info command exposes the assumptions and verification commands behind an estimate. Because those figures are estimates, use the documented benchmark workflow to download and serve a model, measure tokens per second on your own hardware, and save the result locally. The project also describes an optional process for contributing a benchmark result through a pull request. These are project-documented capabilities; recommendation accuracy and benchmark results have not been independently verified here.
Compare measured performance only under the conditions you care about, including the selected model, quantization, context, runtime, and execution mode. A result from one setup should not be treated as a general speed rating for every machine.
Install and compatibility depend on your platform
The project lists macOS (Apple Silicon and Intel), Linux (x86_64 and ARM64), and Windows (x86_64), and describes 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 or Podman, and building from source. These details can change, so use the current installation and compatibility instructions to choose the correct method for your operating system, hardware, and preferred runtime before installing.
Quick Recap
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