What techniques reduce offline AI model loading time?

Model loading optimization techniques: memory-mapped file I/O (mmap) avoids copying model weights from disk to RAM, allowing the OS to page in data on demand. Lazy loading initializes only the layers needed for the first inference, loading remaining layers asynchronously. Model compilation ahead-of-time (AOT) with tools like torch.compile, TVM, or TensorRT converts models to optimized machine code during deployment rather than at startup. Weight deduplication shares common embedding tables across model components. Persistent caching keeps frequently-used models in a RAM disk or tmpfs. Format optimization: GGUF and safetensors formats are designed for fast loading with zero-copy deserialization. Quantized models load faster due to smaller file sizes. For multi-model systems, implement a model pool with LRU eviction and preloading of predicted next models. These techniques combined can reduce a 7B model's load time from 30 seconds to under 2 seconds.