How to Use Run This LLM to Find What Your Hardware Can Run

Use the "I have hardware" search path on runthisllm.com: enter your hardware details, and the site returns local AI models that fit, along with VRAM requirements and performance estimates. This works best when you already own a machine (or have settled on a GPU) and want to know which of the 295+ cataloged models — Llama, Qwen, DeepSeek, Mistral, Gemma, Phi, FLUX, Whisper, and others — are realistic to run. The tool is JavaScript-based, so it must be enabled in your browser before the search will work.

What the site covers

Run This LLM is a hardware requirements database for local AI models. Its catalog spans multiple categories, which matters because "what can I run" depends on the workload type, not just model size:

For each model, the site lists build specs, VRAM requirements, performance estimates, and benchmark scores. That combination is what lets you judge not just "will it load" but "will it run at a usable speed."

Step-by-step: from your hardware to a shortlist

1. Confirm JavaScript is enabled

The page states the tool requires JavaScript. If you see only the heading and a JavaScript notice, enable JS in your browser and reload before going further.

2. Choose the "I have hardware" path

The site presents two entry points: "I have hardware — What can I run?" and "I have a model — What do I need?" For this task, pick the first.

3. Enter your hardware details

Provide the specifics of your machine — most importantly your GPU and its VRAM, since VRAM requirements are the primary filter in the results. The more accurate your input, the more trustworthy the match list.

4. Read the results as a fit list, not a guarantee

For each matching model, check three things:

• VRAM requirement against your actual VRAM, with headroom for context length and runtime overhead.
• Performance estimate — a model that technically fits but runs very slowly may not be worth it for chat or code work.
• Category — confirm the model does the task you want (e.g., FLUX for images, Whisper for audio).

5. Cross-check with the reverse path

If a model you care about doesn't appear in your results, switch to "I have a model — What do I need?" and look up its requirements directly. This tells you whether it's a hard no or just a marginal fit.

Verification and common snags

• Verify a candidate before committing: look up the same model via the reverse search path and compare its stated VRAM requirement and benchmarks with your hardware entry. Consistent numbers across both views are a good sign.
• JavaScript disabled: the most common blocker — the database won't render without it.
• Vague hardware input: entering only a CPU or a generic "gaming PC" produces a less useful list; GPU model and VRAM are the inputs that drive matching.
• Fits but slow: treat performance estimates as the deciding factor for interactive use (chat, code) versus batch jobs (image or audio generation), where slower throughput may be acceptable.

When this approach fits

Use the "I have hardware" path when you're choosing among models for hardware you already own. Use the reverse path when a specific model is the goal and you're deciding whether your current machine is enough or what upgrade would be needed. Either way, the site's per-model build specs, VRAM figures, and benchmarks are the details to check before downloading anything.