20x Faster TRL Fine-Tuning with RapidFire AI (huggingface.co)

Hugging Face’s TRL now officially integrates RapidFire AI, a scheduler and runtime that runs multiple fine-tuning/post-training configurations concurrently — even on a single GPU — to dramatically cut experimentation time. In internal benchmarks RapidFire reports ~16–24× higher throughput vs. sequential trials (examples: 4 configs on 1 GPU reduced time from 120 min to 7.5 min; 8 configs on 1 GPU from 240 min to 12 min), with hardware tests on A100 40GB using TinyLlama and Llama-3.2 models. For practitioners this means you can compare many SFT/DPO/GRPO variants faster, get earlier signals on eval metrics, and iterate to better-performing models without multiplying GPU costs. Technically, RapidFire AI provides drop-in TRL wrappers (RFSFTConfig, RFDPOConfig, RFGRPOConfig) and an adaptive chunk-based scheduler that shards datasets into chunks and cycles configs at chunk boundaries so each config sees incremental, comparable data quickly. It uses efficient shared-memory checkpointing and model spilling/loading to keep GPU utilization high across multi-GPU setups, and exposes Interactive Control Ops (stop, resume, delete, clone-modify with optional warm-start) from an MLflow-based dashboard for real-time intervention. RapidFire is pip-installable and open source, aiming to turn slow, sequential hyperparameter sweeps into fast, hyperparallel experiments that surface winning configurations sooner.