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SUN: Shared Use of Next-token Prediction for Efficient Multi-LLM Disaggregated Serving
arXiv – CS AI|Sunghyeon Woo, Ahreum Seo, Jaegwang Lee, Jaeeun Kil, Hanbae Seo, Joonghoon Kim, Baeseong Park, Se Jung Kwon, Dongsoo Lee||1 views
🤖AI Summary
Researchers propose SUN (Shared Use of Next-token Prediction), a novel approach for multi-LLM serving that enables cross-model sharing of decode execution by decomposing transformers into separate prefill and decode modules. The system achieves up to 2.0x throughput improvement per GPU while maintaining accuracy comparable to full fine-tuning, with a quantized version (QSUN) providing additional 45% speedup.
Key Takeaways
- →SUN enables cross-model batching in multi-LLM serving by sharing frozen decode modules across different models.
- →The approach achieves up to 2.0x throughput improvement per GPU over conventional disaggregation methods.
- →SUN maintains accuracy comparable to full fine-tuning while keeping time-per-output-token within 5%.
- →Quantized SUN (QSUN) provides an additional 45% speedup while preserving shared decoding benefits.
- →The system addresses GPU underutilization issues in memory-bound decoding scenarios, especially under skewed workloads.
#llm-serving#multi-model#gpu-optimization#transformer-architecture#model-sharing#throughput#quantization#decode-execution#resource-efficiency
Read Original →via arXiv – CS AI
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