Road to 5 Million Tokens: Breaking Barriers in Long Context Training — Max Ryabinin, Together AI
Jun 8, 2026 · 15:50
Max Ryabinin from Together AI presents their research on extending transformer context length to 5 million tokens using Untied Ulysses, which cuts activation memory by reusing buffers across attention head iterations. The talk walks through a stack of techniques including fully sharded data parallelism, DeepSpeed Ulysses context parallelism for an 8x activation reduction, activation checkpointing for another 8x, CPU offloading of transformer block inputs, and chunked sequence training. Even with these, training a LLaMA 3B model with 3 million tokens fits on an 8xH100 node, but 5 million requires Untied Ulysses. Instead of allocating one large buffer per attention head group, it chunks heads further and reuses buffers across iterations, cutting activation memory with negligible throughput impact. At both 8B and 32B scale, results match the most memory-optimized transformer training baselines while pushing sequence length 25% further than prior Ulysses implementations.
