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Chaoyi Jiang

2 accepted papers

2026

DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing

ICML 2026poster

Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive prefill and memory-bound decode phases. Existing approaches either (1) aggregate both phases on shared GPUs, leading to interference between prefill and d…

Cited by 0SourceScholar
2025

KVPR: Efficient LLM Inference with I/O-Aware KV Cache Partial Recomputation

ACL 2025finding

Inference for Large Language Models (LLMs) is computationally demanding. To reduce the cost of auto-regressive decoding, Key-Value (KV) cache is used to store intermediate activations, which significantly lowers the computational overhead for token generation. However, the memory required for the KV…