What LLM Can I Run?

Our compatibility checker is designed to go beyond simple model downloads. It first performs a detailed, one-click scan of your system's GPU and available Video RAM (VRAM). Since LLM performance is heavily constrained by VRAM, this tool calculates exactly which models—like Llama 3 or Gemma—will fit within your hardware limits.

It analyzes several critical factors:

• VRAM Capacity: Determines if the model's weights can be loaded entirely onto the GPU for maximum speed.
• Offloading Potential: If a model is slightly too large, we check if it can run effectively by 'offloading' some layers to your system's main CPU RAM.
• Model Quantization: We account for various formats (like GGUF), which compress models without losing critical performance characteristics.

By reviewing these metrics, you get an accurate, ranked list of 50+ models that are genuinely runnable on your specific machine.

Once you've identified compatible models, optimizing your setup can dramatically improve the user experience. These tips help maximize throughput and minimize latency:

• Prioritize GGUF Format: When running models locally, always seek out the GGUF format. This is the industry standard for optimized CPU/GPU hybrid performance and quantization.
• Start Small and Scale Up: Begin with a smaller model (like a 3B or 7B parameter version) to benchmark your system's speed and stability before attempting massive models like DeepSeek 67B.
• Manage Context Window Size: If you are doing multi-step reasoning, keep the input prompt concise. A shorter context window uses less VRAM per token generated, improving overall efficiency.

Regularly check your system's temperature and monitor your GPU utilization while running inference; overheating is a common cause of performance degradation.