AI Research
Dual-Flow Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation
Decoupling prefill and decode phases to optimize inference costs in large language models.
As large language models serve more requests, cumulative inference cost is becoming increasingly important relative to one-time training cost. The two inference phases stress hardware differently: prompt prefill is parallel and typically compute-bound, whereas autoregressive deco...
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ArXiv CS.AI
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ArXiv CS.AI
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The Dual-Flow Transformer revolutionizes inference efficiency by decoupling the compute-heavy prompt prefill phase from the sequential decode process. This architectural innovation allows models to allocate more intelligence to token generation without inflating the costs associated with initial context processing.
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