RapidFire AI Launches Breakthrough Open-Source Engine for LLM Fine‒Tuning and Post‒Training

Achieve 20× higher experimentation throughput with parallel execution and realâ€'time control–on the same hardware

GlobeNewswire

September 23, 2025

MOUNTAIN VIEW, Calif., Sept. 23, 2025 (GLOBE NEWSWIRE) — RapidFire AI today announced the openâ€'source release of its “rapid experimentation” engine designed to dramatically speed up and simplify one of the most critical, yet underserved, stages of AI development: customizing large language models (LLMs) through fineâ€'tuning and postâ€'training.

Released under the Apache 2.0 license, RapidFire AI lets you launch and compare many fine-tuning/post-training configs at once on a single GPU or across multiple GPUs spanning data, model/adapter choices, trainer hyperparameters, and reward functions. It does this by training on dataset chunks and efficiently swapping adapters or base models between chunks, while the scheduler automatically reallocates GPUs for high utilization. Live metrics stream to an MLflow dashboard from where you can stop, resume, and clone-modify configurations, enabling faster, cheaper exploration toward better eval metrics.

“As organizations customize LLMs for specific domains, the ability to iterate quickly and intelligently across fineâ€'tuning and postâ€'training workflows becomes critical,” said Faraz Shafiq, Worldwide Tech Leader, Generative AI at AWS. “RapidFire AI fills a key gap by bringing structure, speed, and control to where it matters most.”

Built for Hyperparallel Exploration and Interactive Control

RapidFire AI enables users to launch as many training/tuning configurations as they want in parallel even on a single multiâ€'GPU machine, spanning variations of base model architectures, hyperparameters, adapter specifics, data preprocessing, and reward functions. Live metrics and Interactive Control (IC) Ops allow users to stop weak configurations early, clone highâ€'performers, and warmâ€'start new configurations in real time right from the dashboard, enabling more impactful results without needing more GPU resources. In the same wallâ€'time as a few sequential comparisons, teams can explore far more paths and reach better metrics, often realizing 20× higher experimentation throughput.

“Many developers are moving beyond prompting to fineâ€'tuning LLMs for accuracy, reliability, and cost,” said Andrew Ng, Managing General Partner at AI Fund. “Arun, Jack, and the RapidFire AI team are giving developers the ability to run dozens of experiments in parallel, use advanced methods like GRPO with wellâ€'designed reward functions, and accelerate building wellâ€'tuned models.”

Key Capabilities

• Hyperparallel configuration comparison on a single machine: compare even 20+ variants in parallel; expand or prune on the fly based on data- and use case-specific constraints.
• Interactive Control (IC) Ops: Stop, Resume, Cloneâ€'Modify, and warmâ€'start new configurations directly from the dashboard on the fly to double down on what works.
• Chunkâ€'wise scheduling: Adaptive engine cycles configurations over chunks of the data to maximize GPU utilization, while ensuring sequential-equivalent metrics and minimizing runtime overheads.
• Hugging Faceâ€'native workflow: Works natively with PyTorch, Transformers, TRL; supports PEFT/LoRA and quantization.
• Supported TRL workflows: SFT, DPO, and GRPO.
• MLflowâ€'based dashboard: Unified tracking and visualization for all metrics, metadata management, and control panel for IC Ops–no extra MLOps wiring needed.

RapidFire AI's technology is rooted in award-winning research by its Co-founder, Professor Arun Kumar, a faculty member in both the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego.

“RapidFire AI is built for how AI teams actually work–messy, fastâ€'moving, and highly iterative,” said Arun Kumar, Coâ€'Founder and CTO of RapidFire AI. “We are ushering in a more structured, evidenceâ€'driven paradigm of AI development–less dependent on intuition or folklore–where high experimentation throughput makes it possible to simultaneously reduce training time, keep costs under control, and truly realize the power of modern AI for their internal data.”

The company has raised $4 million in pre-seed funding from leading deepâ€'tech investors including .406 Ventures, AI Fund, Willowtree Investments, and Osage University Partners.

Availability

RapidFire AI's openâ€'source package, documentation, and quickstart guides are available now: rapidfire.ai/docs

AI developers and researchers are invited to try out this package, share feedback, showcase their use cases, and contribute to extensions. For more information on the company visit www.rapidfire.ai.

About RapidFire AI

RapidFire AI is an openâ€'source system for accelerating the customization, fineâ€'tuning, and postâ€'training of LLMs. RapidFire AI lets users run, compare, and dynamically control multiple configurations in real time. Backed by .406 Ventures, AI Fund, Osage University Partners, and Willowtree Investments, RapidFire AI helps teams iterate faster, maximize compute efficiency, and deliver better AI results with less friction.

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