Can I Run This LLM? Local AI Hardware Checker

Categories: Coding & Developer Tools, Productivity, Research | Pricing: Free | Official Website ↗

llmrun.dev helps users determine which AI models their local hardware (GPU, Mac) can run, checking VRAM, compatibility, and expected performance.

llmrun.dev is a tool designed to assist users in identifying compatible AI models for their specific local hardware. It allows users to select their GPU or Apple Silicon device and then provides a compatibility rating (S to F) for various AI models based on VRAM requirements and expected performance. The platform also offers model rankings by a composite score, categorized by VRAM tiers and use cases (e.g., coding, vision, long context). Users can then find instructions to run compatible models via Ollama, LM Studio, or by downloading GGUF files. The website features detailed information on popular GPUs, MacBooks, and AI boxes, including specifications like VRAM, bandwidth, CUDA cores, and TDP. It also provides profiles for popular open-source LLMs, detailing their parameter counts, VRAM requirements at different quantization levels, context window sizes, and licenses. The tool aims to simplify the process of setting up local AI inference by matching hardware capabilities with model demands.

Key Features

Automatic hardware detection (in-browser)
Manual hardware selection (GPU/Mac)
AI model compatibility check (VRAM, performance)
Model rankings by composite score
Model filtering by VRAM tier
Model filtering by use case (coding, vision, long context)
Detailed model profiles (parameters, VRAM, context, license)
Hardware profiles (GPUs, Macs, AI boxes)

Pros

Directly checks hardware compatibility for local AI models.
Provides clear performance estimates (tokens/second).
Offers model rankings and filtering by VRAM and use case.
Supports a wide range of GPUs and Apple Silicon devices.
Guides users on how to run models locally with popular tools.

Cons

Limited to local inference setup, not cloud-based AI.
Does not offer direct model downloads or hosting.
Information is based on pre-calculated benchmarks, not real-time testing.
No mention of support for non-GPU hardware for inference (e.g., CPU-only).
Relies on user to manually install models after checking compatibility.

Use Cases

Determining if a specific LLM can run on existing hardware.
Comparing performance of different LLMs on a given GPU.
Identifying the best LLMs for a new hardware purchase.
Finding suitable LLMs for specific local AI tasks (e.g., coding, vision).

Best For

AI enthusiasts
Developers running local LLMs
Users planning AI hardware upgrades
Researchers evaluating local model performance

Integrations: Ollama, LM Studio, GGUF

Platforms: Web

Watch demo on YouTube ↗

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