The post NVIDIA Enhances Training Throughput with NeMo-RL’s Megatron-Core appeared on BitcoinEthereumNews.com. Ted Hisokawa Aug 20, 2025 16:26 NVIDIA introduces Megatron-Core support in NeMo-RL v0.3, optimizing training throughput for large models with GPU-optimized techniques and enhanced parallelism. NVIDIA has unveiled the latest iteration of its NeMo-RL framework, version 0.3, which incorporates support for Megatron-Core. This enhancement aims to optimize training throughput for large language models by leveraging GPU-optimized techniques and advanced parallelism strategies, according to NVIDIA’s official blog. Challenges with Previous Backends The initial release of NVIDIA NeMo-RL utilized PyTorch DTensor (FSDP2), offering native integration with the HuggingFace ecosystem and enabling quick experimentation through PyTorch’s native parallelisms. However, as model sizes increased to hundreds of billions of parameters, the DTensor path proved inadequate due to significant recompute overhead and lack of optimized NVIDIA CUDA kernels, leading to inefficient step times. Introducing Megatron-Core The Megatron-Core library addresses these limitations by offering a more efficient solution for training extensive models. It employs a 6D parallelism strategy to enhance communication and computation patterns, supporting various model architectures. This backend enables seamless training of massive language models, enhancing throughput and performance significantly. Getting Started with Megatron-Core Implementing Megatron-based training involves adding specific configurations to the YAML setup. The process is streamlined by NeMo-RL, which handles complex tuning automatically, presenting users with straightforward configuration options. This makes the adoption of Megatron-Core more accessible for developers, allowing them to focus on optimizing their model training processes. Performance Improvements Megatron-based training supports both dense and Mixture of Experts (MoE) models. Performance tests have demonstrated superior training performance with Megatron-Core compared to PyTorch DTensor, as shown in various model configurations like Llama 3.1-8B and 70B. The enhancements are evident in faster step times and improved convergence properties. Additional Features and Future Prospects NeMo-RL v0.3 introduces features such as async rollouts and non-colocated… The post NVIDIA Enhances Training Throughput with NeMo-RL’s Megatron-Core appeared on BitcoinEthereumNews.com. Ted Hisokawa Aug 20, 2025 16:26 NVIDIA introduces Megatron-Core support in NeMo-RL v0.3, optimizing training throughput for large models with GPU-optimized techniques and enhanced parallelism. NVIDIA has unveiled the latest iteration of its NeMo-RL framework, version 0.3, which incorporates support for Megatron-Core. This enhancement aims to optimize training throughput for large language models by leveraging GPU-optimized techniques and advanced parallelism strategies, according to NVIDIA’s official blog. Challenges with Previous Backends The initial release of NVIDIA NeMo-RL utilized PyTorch DTensor (FSDP2), offering native integration with the HuggingFace ecosystem and enabling quick experimentation through PyTorch’s native parallelisms. However, as model sizes increased to hundreds of billions of parameters, the DTensor path proved inadequate due to significant recompute overhead and lack of optimized NVIDIA CUDA kernels, leading to inefficient step times. Introducing Megatron-Core The Megatron-Core library addresses these limitations by offering a more efficient solution for training extensive models. It employs a 6D parallelism strategy to enhance communication and computation patterns, supporting various model architectures. This backend enables seamless training of massive language models, enhancing throughput and performance significantly. Getting Started with Megatron-Core Implementing Megatron-based training involves adding specific configurations to the YAML setup. The process is streamlined by NeMo-RL, which handles complex tuning automatically, presenting users with straightforward configuration options. This makes the adoption of Megatron-Core more accessible for developers, allowing them to focus on optimizing their model training processes. Performance Improvements Megatron-based training supports both dense and Mixture of Experts (MoE) models. Performance tests have demonstrated superior training performance with Megatron-Core compared to PyTorch DTensor, as shown in various model configurations like Llama 3.1-8B and 70B. The enhancements are evident in faster step times and improved convergence properties. Additional Features and Future Prospects NeMo-RL v0.3 introduces features such as async rollouts and non-colocated…