**Show HN: LoongForge-Train LLMs, VLMs, diffusion and embodied models, faster**

> A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs. - GitHub - baidu-baige/LoongForge: A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs. English | 简体中文 Train LLMs, VLMs, diffusion and embodied models, faster. 🌐 Website · 📖 Docs · ✍️ Blog · ⚡ Quick Start · 📊 Performance · 🏛️ Supported Models · 💬 Contact Us Example: embodied-model training on LoongForge — DreamZero at 4.38× baseline throughput, loss curves aligned 💡 Why LoongForge? LoongForge is an open-source training framework developed by the Baidu AI Cloud Baige team , built to deliver faster training for mainstream LLMs, VLMs, diffusion, and embodied models, thereby significantly reducing costs. Easy to Use — Ready-to-run configs and launch examples for supported models, covering pre-training, continued pre-training, SFT, and LoRA. High Performance — Built on multiple training backends (Megatron-LM and torch-native), with deep optimizations for each model family across parallelism strategy, memory footprint, communication overlap, and kernel efficiency, while keeping training loss curves aligned with the baseline . Proven at Scale — Open-sourced from AIAK-Training-LLM , a training acceleration suite serving enterprise customers in Education, Computer Vision, and Embodied AI, with the largest production runs reaching 5,000+ XPUs . 🐉 LoongForge is named after the traditional Chinese loong boat (龙舟) , a symbol of coordinated power and forward momentum. 🏗️ Architecture Since optimal training strategies differ across model families and scales, LoongForge adopts a multi-backend architecture. Megatron Stack — For LLMs, VLMs, and diffusion models. Powered by a patched Megatron-LM and extended with MoE parallelism, per-component heterogeneous parallelism, long-sequence optimizations, etc. Torch-Native Stack — For embodied models (VLA and WAM). A standalone torch-native subsystem featuring DDP / ZeRO-1 / FSDP / HSDP , with deep optimizations for representative models across I/O, communication strategy, kernel efficiency, etc. 🔥 Latest News \[2026/09\] ✨ Added Kimi-K3 BF16 training support for both LLMs and VLMs. \[2026/09\] ⚡ Added an optimized DreamZero Wan2.2-5B FSDP recipe with cache-aware data loading, compiled attention blocks, frozen-module handling, and Delta-FP8 AllGather. \[2026/08\] 🤖 Added VLA training support for Wall-OSS-0.5 , with custom fused operators for higher training throughput. \[2026/08\] 📄 Released the TAOT paper — topology-aware dynamic expert replica placement that tackles expert-parallel ( EP ) load imbalance in MoE training, cutting overhead by up to 74% over industry solutions, with 1.43× speedup measured on a real training case. \[ blog \] \[2026/08\] ✨ Added training support for GLM-5.2 , along with a GLM-5.2 + MoonViT custom-composition example for extending GLM with multimodal capabilities. \[2026/08\] ✨ Added training support for MiniCPM-V-4.6 and Qwen3.8-27B . \[2026/08\] 🧪 Introduced a unified evaluation module for the embodied stack, currently covering Pi0.5 / xVLA / GR00T , with more models on the way. \[2026/07\] 🐳 Unified the prebuilt Docker images — all model families (LLM / VLM / VLA / Diffusion) now share a single image. \[2026/07\] 🤖 Released LoongForge-Embodied , a torch-native DDP/FSDP training subsystem for embodied models (Pi0.5, GR00T-N1.6/N1.7, xVLA, LingBot-VA, FastWAM, DreamZero, and Cosmos3), with up to 4.38× speedup . \[ blog \] \[2026/07\] ✨ Added training support for Qwen-Image-Edit-2511 . \[2026/07\] ✨ Added training support for DeepSeek-V4-Flash / DeepSeek-V4-Pro . 📅 More \[2026/06\] 🤖 Expanded VLA coverage with GR00T N1.6 ; 2.3× speedup on GR00T...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：ai-products
- **发布时间**：2026-09-15 23:03（北京时间）
- **原文**：[打开原文](https://github.com/baidu-baige/LoongForge)