Show HN: RapidFire AI: 16–24x More Experiment Throughput Without Extra GPUs (github.com)

RapidFire AI is an experiment execution framework designed to radically speed up LLM and deep‑learning fine‑tuning workflows by hyperparallelizing experiments (the project claims 16–24× higher throughput) without adding GPUs. It uses interruptible, chunk‑based scheduling to interleave many configurations at the granularity of data chunks so multiple runs can make visible progress concurrently—even on one GPU—while a dashboard offers real‑time Interactive Control Ops (stop, resume, clone/modify, warm‑start from parent weights). The scheduler dynamically allocates GPU resources across runs and supports Grid/Random search and AutoML integration, enabling fast side‑by‑side comparisons and rapid iteration. Under the hood RapidFire AI uses a microservices‑inspired stack: a Dispatcher REST API, Controller (orchestrator), GPU Workers that train chunk-by-chunk, a dashboard, and SQLite-backed state. It ships as a Python package (Python 3.12), requires Nvidia GPUs with compute capability 7.x/8.x, CUDA 11.8+, PyTorch 2.7.1+ (with matching CUDA builds), and integrates a forked MLflow for tracking and a custom IC Ops panel. Implications for ML teams: much faster hyperparameter sweeps, cheaper experimentation via warm‑starts and cloning, and better GPU utilization. Caveats include per‑run single‑GPU memory limits, single‑machine/SQLite scaling constraints, and dependency/setup complexity (vllm/flash‑attn, node versions, specific CUDA/PyTorch builds).