Stop Renting Intelligence: The Train-to-Deploy Loop for Specialized AI — Fireworks AI

Closed models are getting expensive fast. Open models can match them, if you keep training them on your own data. Jetashree Ravi, who leads part of the applied machine learning team at Fireworks AI, explains how teams move from closed models to open ones without losing quality. Her three truths: open models like GLM 5.2 are production-ready, customization closes the remaining quality gap, and the AI stack is shifting to a continuous train-and-serve loop. She shares Fireworks' scale (about 30 trillion tokens, 10,000+ companies) and customer results: Cursor's Composer 2 trained and hosted on Fireworks with 13x faster inference, a 40x latency improvement for Vercel, and 12% better quality for Genspark with reinforcement fine-tuning. She covers the FireAttention engine and its custom CUDA kernels, speculative decoding with custom draft models, and three ways to train (a training agent, managed training, and a training API for SFT, RFT and DPO), then the flywheel: one-click deploys, monitoring, hot-loading new weights with no downtime, and matching numerics between training and serving. She ends with demos of the training agent and managed training. In this talk: • Why open models plus customization can match closed ones • FireAttention, custom kernels and speculative decoding • Training agent, managed training and the training API • Hot loading and numerical fidelity for a continuous train-and-deploy loop SPEAKER Jetashree Ravi, Tech Lead Manager, Applied ML, Fireworks AI LinkedIn: https://www.linkedin.com/in/jetashree-ravi/ GitHub: https://github.com/jraviS2023 LINKS Fireworks AI: https://fireworks.ai Managed fine-tuning docs: https://docs.fireworks.ai/fine-tuning/managed-finetuning-intro Supervised fine-tuning docs: https://docs.fireworks.ai/fine-tuning/supervised-fine-tuning CHAPTERS 0:00 Intro 1:27 Three truths about open models 2:22 Where Fireworks fits 3:12 Fireworks at scale 3:47 Cursor, Vercel and Genspark 5:27 FireAttention and custom kernels 6:27 Speculative decoding and draft models 7:22 Three ways to train 8:22 Managed training 9:27 Training API 10:07 SFT, RFT and DPO 11:07 The data flywheel 12:42 Hot loading new weights 13:22 Numerical fidelity 14:22 Demo: training agent 16:17 Demo: managed training and deploy 18:42 Wrap-up Recorded at the AI Engineer World's Fair 2026 in San Francisco. Subscribe…