Peter Jang
June 3, 2025 03:11
NVIDIA’s NEMO framework introduces efficient technologies for long -term text LLM training, optimizes performance for models that solve memory problems and handle millions tokens.
NVIDIA has announced significant developments that can improve efficiency and performance by using NEMO framework by handling millions of tokens in the training of LLM (Lange Language Models). According to NVIDIA, this development deals with increasing demand for models that can handle a wide range of context lengths, which are important for applications such as video creation, legal analysis and AI -centric language translation.
Extended context is required
As the LLM continued to develop, the ability to manage and process long data sequences was essential. Models with extended context lengths can maintain consistency or manage complex reasoning work in thousands of video frames. NVIDIA’s deepSeek-R1 and LLAMA NEMOTRON illustrate models that benefit from these features, and the context length reaches 128k and more than 10 million tokens, respectively.
Challenge of long -term text education
Training a long LLM is especially important for memory management. The computational complexity of the transformer -based LLMS increases exponentially depending on the length of the sequence, and the traditional training method is expensive. NVIDIA solves these problems with some innovative technologies in NEMO framework.
Nemo framework’s innovative technology
NEMO framework introduces memory efficient strategies such as activation re -calculation, context parallel processing and activation off loading. Re -calculation of activation is optionally stored and re -calculated during training to reduce memory usage, allowing longer sequences without exceeding the GPU memory limit.
The context parallel processing (CP) distributes sequence to several GPUs to further improve training efficiency. This approach can minimize the memory footprints and the overhead cost of calculations to train the model in a longer sequence without a performance deterioration.
The activation off -road transmits intermediate activation and inactive weights to CPU memory to make up for these technologies to effectively expand the GPU memory capacity of large models.
Performance and expansion
NVIDIA’s approach showed significant improvements in training performance for sequence lengths in 16K to millions of tokens. NEMO framework’s CP and other technology implementation ensure the efficient use of computer resources, maintaining high terraflop performance even in the extension sequence length.
conclusion
Nemo frameworks of NVIDIA offer a comprehensive solution for training LLMs with long context lengths, optimizing memory use and calculation efficiency. Using these innovations, developers can train a high -end model that meets the demands of modern AI applications. The tested recipes and documents of the framework provide a powerful foundation for expanding the context and improving model performance.
Image Source: Shutter Stock