Can I Run AI Locally? Hardware Checker

Our Hardware Checker provides a rapid, one-click analysis of your computer's capabilities specifically for running large language models (LLMs) locally. We don't require you to upload sensitive files; the detection happens entirely within your browser.

By instantly detecting key components like your GPU model and available VRAM, we can calculate realistic performance metrics. You will immediately see: Which open LLMs (e.g., Llama 3 variants) are compatible with your current setup, the expected token generation speeds (tokens/second), and crucial information regarding potential bottlenecks.

Furthermore, if you're interested in running more powerful models that are just outside your reach, we provide clear recommendations on the minimum necessary upgrade—whether it’s increasing VRAM or optimizing system RAM—to get started without guesswork.

To achieve the best possible speed and quality when running local AI, focus on model quantization. Quantization techniques (like GGUF) significantly reduce the VRAM footprint without catastrophic loss of performance.

• Prioritize 4-bit or 5-bit quantized models when VRAM is limited.
• Ensure your operating system and drivers are fully updated, as performance optimizations are frequently released for NVIDIA/AMD hardware.
• When testing, always use a benchmark task (like summarizing a 5000-word document) rather than just generating single prompts to get an accurate speed reading.

Remember that optimizing your workflow—by selecting the model size appropriate for your hardware—is more effective than chasing the absolute biggest, most demanding LLMs.