Posts by Hao Chen
Day-0 Support for the SGLang-Native RL Framework - slime on AMD Instinct™ GPUs
- 25 September 2025
AMD is excited to provide Day-0 support for the SGLang-native RL framework, slime. In this post, we will provide more details about our support and optimizations, as well as slime’s benefits for large-scale RL training. First, we describe the engineering efforts behind slime—including codebase modification, kernel-level memory management for ROCm™ software, and modifications to third-party dependencies (Megatron-LM, SGLang, and torch_memory_saver)—as well as Docker images that enable efficient execution on AMD Instinct™ GPUs. Architecturally, slime supports two training modes: synchronous and asynchronous. Across these modes, we additionally present system-level optimizations with the corresponding use cases. Specifically, in the synchronous setting, our rollout optimizations deliver a 40% throughput improvement over the one without it on AMD Instinct™ GPUs. In the asynchronous setting, we develop a multi-turn RL agent framework to train the kernel generation model. You can also read more about this support in the MLsys – SGLang official blog.
Instella-T2I: Open-Source Text-to-Image with 1D Tokenizer and 32× Token Reduction on AMD GPUs
- 15 July 2025
In this blog, we introduce Instella T2I, text-to-image models in the AMD open-source Instella model family built from scratch on AMD Instinct™ MI300X GPUs. We’ll walk through the model architecture, training pipeline, tokenizer innovations, and how the system scales efficiently across MI300X GPUs. Instella-T2I v0.1 sets a new baseline for scalable, high-resolution open-source text-to-image generation. You will also explore how AMD is helping advance this space—and how you can get started with the model today. In Instella-T2I, we build upon the rapid advancements in large language models (LLMs) and investigate the use of decoder-only models as text encoders in T2I models as shown in Figure 1.