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## 🚀 Speed Benchmark
## 🎉 News - **\[2025/09\]** XTuner V1 Released! A Next-Generation Training Engine Built for Ultra-Large MoE Models ## 📖 XTuner V1 XTuner V1 is a next-generation LLM training engine specifically designed for ultra-large-scale MoE models. Unlike traditional 3D parallel training architectures, XTuner V1 is optimized for the mainstream MoE training scenarios prevalent in today's academic research. ### Key Features **📊 Dropless Training** - **Scalable without complexity:** Train 200B-scale MoE models without expert parallelism; 600B models require only intra-node expert parallelism - **Optimized parallelism strategy:** Smaller expert parallelism dimension compared to traditional 3D approaches, enabling more efficient Dropless training **📝 Long Sequence Support** - **Memory-efficient design:** Train 200B MoE models on 64k sequence lengths without sequence parallelism through advanced memory optimization techniques - **Flexible scaling:** Full support for DeepSpeed Ulysses sequence parallelism with linearly scalable maximum sequence length - **Robust performance:** Maintains stability despite expert load imbalance during long sequence training **⚡ Superior Efficiency** - **Massive scale:** Supports MoE training up to 1T parameters - **Breakthrough performance:** First to achieve FSDP training throughput that surpasses traditional 3D parallel schemes for MoE models above 200B scale - **Hardware optimization:** Achieves training efficiency on Ascend A3 Supernode that exceeds NVIDIA H800
## đŸ”Ĩ Roadmap XTuner V1 is committed to continuously improving training efficiency for pre-training, instruction fine-tuning, and reinforcement learning of ultra-large MoE models, with special focus on Ascend NPU optimization. ### 🚀 Training Engine Our vision is to establish XTuner V1 as a versatile training backend that seamlessly integrates with the broader open-source ecosystem. | Model | GPU(FP8) | GPU(BF16)| NPU(BF16) | |------------|-----------|----------|-----------| | Intern S1 | ✅ | ✅ | ✅ | | Intern VL | ✅ | ✅ | ✅ | | Qwen3 Dense| ✅ | ✅ | ✅ | | Qwen3 MoE | ✅ | ✅ | ✅ | | GPT OSS | ✅ | ✅ | 🚧 | | Deepseek V3| ✅ | ✅ | 🚧 | | KIMI K2 | ✅ | ✅ | 🚧 | ### 🧠 Algorithm The algorithm component is actively evolving. We welcome community contributions - with XTuner V1, scale your algorithms to unprecedented sizes! **Implemented** - ✅ **Multimodal Pre-training** - Full support for vision-language model training - ✅ **Multimodal Supervised Fine-tuning** - Optimized for instruction following - ✅ [GRPO](https://arxiv.org/pdf/2402.03300) - Group Relative Policy Optimization **Coming Soon** - 🔄 [MPO](https://arxiv.org/pdf/2411.10442) - Mixed Preference Optimization - 🔄 [DAPO](https://arxiv.org/pdf/2503.14476) - Dynamic Sampling Policy Optimization - 🔄 **Multi-turn Agentic RL** - Advanced agent training capabilities ### ⚡ Inference Engine Integration Seamless deployment with leading inference frameworks: - [x] LMDeploy - [ ] vLLM - [ ] SGLang ### Data Preparation - You can use [GraphGen](https://github.com/open-sciencelab/GraphGen) to create synthetic data for fine-tuning. ## 🤝 Contributing We appreciate all contributions to XTuner. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. ## 🙏 Acknowledgement The development of XTuner V1's training engine has been greatly inspired by and built upon the excellent work of the open-source community. We extend our sincere gratitude to the following pioneering projects: **Training Engine:** - [Torchtitan](https://github.com/pytorch/torchtitan) - A PyTorch native platform for training generative AI models - [Deepspeed](https://github.com/deepspeedai/DeepSpeed) - Microsoft's deep learning optimization library - [MindSpeed](https://gitee.com/ascend/MindSpeed) - Ascend's high-performance training acceleration library - [Megatron](https://github.com/NVIDIA/Megatron-LM) - NVIDIA's large-scale transformer training framework **Reinforcement Learning:** XTuner V1's reinforcement learning capabilities have been enhanced through insights and best practices from: - [veRL](https://github.com/volcengine/verl) - Volcano Engine Reinforcement Learning for LLMs - [SLIME](https://github.com/THUDM/slime) - THU's scalable RLHF implementation - [AReal](https://github.com/inclusionAI/AReaL) - Ant Reasoning Reinforcement Learning for LLMs - [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF) - An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray We are deeply grateful to all contributors and maintainers of these projects for advancing the field of large-scale model training. ## 🖊ī¸ Citation ```bibtex @misc{2023xtuner, title={XTuner: A Toolkit for Efficiently Fine-tuning LLM}, author={XTuner Contributors}, howpublished = {\url{https://github.com/InternLM/xtuner}}, year={2023} } ``` ## License This project is released under the [Apache License 2.0](LICENSE). Please also adhere to the Licenses of models and datasets being used.