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Untied Ulysses enables large-scale LLM training with 3M token context

By PulseAugur Editorial · [1 sources] ·

Training large language models with extensive context windows, such as 3 million tokens, faces memory limitations on hardware like 8xH100 nodes. Researchers have developed a method called Untied Ulysses to overcome these constraints, enabling the training of models at 8B and 32B scales with significantly longer sequences than previously possible. AI

IMPACT Enables training of larger models with significantly longer context windows, pushing the boundaries of LLM capabilities.

RANK_REASON The item describes a new research method for training LLMs with long context windows. [lever_c_demoted from research: ic=1 ai=1.0]

Read on X — Together (inference / OSS) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Untied Ulysses enables large-scale LLM training with 3M token context

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The item describes a new research method for training LLMs with long context windows. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, model release
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High
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118 days old
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COVERAGE [1]

  1. X — Together (inference / OSS) TIER_1 English(EN) · togethercompute ·

    Training a Llama 3B model with a 3M token context on a single 8xH100 node fails because model parameters alone exhaust GPU memory. @m_ryabinin explains how Unti

    Training a Llama 3B model with a 3M token context on a single 8xH100 node fails because model parameters alone exhaust GPU memory. @m_ryabinin explains how Untied Ulysses, his team's latest research, pushes past that wall, training at 8B and 32B scale with 25% longer sequences

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