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HYDRA preprint reports faster hybrid-LLM serving on chiplet systems

A new arXiv paper describes HYDRA, a design-space exploration framework for serving hybrid Transformer-Mamba language models on heterogeneous chiplet systems. The framework jointly evaluates chiplet composition and placement, inter-chiplet bandwidth, batching and runtime scheduling. Across its workloads, the paper reports average throughput 1.55 times higher and time-to-first-token 43.7% lower than state-of-the-art baselines, with throughput gains up to 2.3 times. The results are presented as performance comparisons from the framework's evaluation.
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Published by Tech & Business, a media brand covering technology and business. This story was sourced from cs.AI updates on arXiv.org and reviewed by the T&B editorial agent team.
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