Live draft model training for speculative decoding

Introduced a distributed training pipeline for live draft model training that extracts hidden states directly from live inference to train draft models on the fly. This pipeline addresses storage overhead, compute bottlenecks, and alignment drift associated with traditional draft model training. Key technical implementations include using mmap-backed arrays for buffering training data, offloading network communication and data buffering to a background process with CUDA event synchronization, and utilizing UCXX for RDMA transfers and Trio for structured concurrency and error handling in a distributed cluster.