About LocalAIRun.com
An independent planning guide for choosing local AI models, hardware, tools, and runnable workflows.
Why this site exists
Local AI changes quickly. A model name alone does not tell you whether it will fit: quantization, context length, runtime support, system memory, GPU memory, and driver maturity can all change the answer. LocalAIRun turns those moving parts into a practical shortlist and, where possible, a documented recipe.
The project is independently maintained and is not affiliated with a model lab, hardware vendor, runtime developer, or cloud provider. Recommendations are intended to help you investigate a setup, not to replace workload-specific testing or a purchasing decision.
What is actually tested
Maintainer testing currently uses a 16GB M4 MacBook Pro. A page is only described as maintainer-tested when that workflow has been run and a result is recorded.
Other model and hardware pages combine official specifications, model cards, runtime documentation, public benchmark results, community reports, and calculated memory estimates. Those are useful planning signals, but they are not presented as first-hand tests.
What you can do here
Start with a task
Compare model classes, quantized artifacts, compatible hardware, and a rough budget for the work you want to do.
Start with hardware
Enter a Mac, GPU, AI PC, system RAM, GPU count, and context target to find models that are likely to fit.
Choose a tool and workflow
Move from a model recommendation to a runtime, desktop app, self-hosted tool, or step-by-step execution recipe.
Return real-world evidence
Submit an anonymous structured result so estimated compatibility can become tested compatibility.
Build private home AI infrastructure
Explore local-first home AI services, network boundaries, storage, automation, and hardware that can remain under your control.
Inspect the underlying libraries
Browse model, hardware, tool, variant, license, runtime, and evidence details instead of relying on a single score.
Editorial and recommendation principles
- 01
Evidence is labeled
Maintainer tests, community reports, official specifications, public benchmarks, and calculated estimates are different kinds of evidence and should not be presented as equivalent.
- 02
Fit before rank
A highly ranked model is not useful if the selected artifact, context length, or runtime cannot work on your hardware. Compatibility is evaluated before preference signals.
- 03
Variants matter
FP16, FP8, Q8, Q5, Q4, GGUF, MLX, and runtime-specific builds can have materially different memory needs and behavior.
- 04
Avoid false precision
Memory and price values are planning estimates. Actual usage changes with cache size, runtime overhead, offload, thermals, drivers, and local prices.
- 05
Corrections are part of the product
Structured reports and page-level feedback are used to challenge assumptions, improve recipes, and update future recommendations.
Privacy, funding, and independence
The planners do not require an account. Anonymous execution reports are submitted only when you choose to send one; a private receipt is kept in your browser so you can check its status. See the privacy policy for analytics, advertising, and report-handling details.
The site may display advertising. Advertising does not determine model rankings, hardware-fit results, or editorial conclusions. There are currently no paid ranking placements; any future sponsorship or commercial relationship will be labeled where it appears.
Corrections and contributions
Local AI results vary by runtime version, operating system, drivers, model artifact, context length, and workload. If a recommendation does not match your real setup, that evidence is useful. Submit a structured result from a recipe, report the affected page, or email [email protected].
Maintained independently by LocalAIRun.com.