Local LLM VRAM Calculator

For a 7B or 8B parameter model using standard 4-bit quantization (like Llama 3.1 8B), you typically need about 6GB to 8GB of VRAM. This capacity comfortably fits the model's weights alongside a standard 8K context window for typical chat and coding tasks.

Quantization compresses an AI model's weights to drastically reduce memory usage. For local AI running on consumer hardware, 4-bit quantization (Q4_K_M) is the industry standard. It shrinks the model by over 60% while maintaining near-perfect reasoning quality and accuracy.

The Context Window dictates how much conversational history or document text the model can "remember" at one time. This memory is stored actively in the GPU's KV Cache. While modern models use Grouped Query Attention (GQA) to keep this efficient, expanding your context to massive sizes (like 128K) for document analysis can still consume an additional 16GB to 40GB of VRAM depending on the model.

Yes, but typically only on Apple Silicon MacBooks (like the M4 or M5 Max) equipped with 64GB or 128GB of Unified Memory. Because standard Windows laptops currently max out at 24GB of dedicated VRAM (e.g., RTX 5090), you would need an enterprise multi-GPU desktop setup to run a 70B model natively on Windows.

Operating systems like Windows (via Desktop Window Manager) and macOS reserve a baseline amount of VRAM just to run your displays and background apps. If you try to fill 100% of your GPU with an AI model, your system will crash with an Out-Of-Memory (OOM) error. We add a 1.5GB safe buffer to ensure your local AI agent runs smoothly without crashing your PC.