Hossein Entezari Zarch

collaborators

6 papers

cs.LG2026

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

Lei Gao, Chaoyi Jiang, Hossein Entezari Zarch +3

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 ph…

cs.CL2026

DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning

Hossein Entezari Zarch, Lei Gao, Chaoyi Jiang +1

Large reasoning models (LRMs) achieve state-of-the-art performance on challenging benchmarks by generating long chains of intermediate steps, but their inference cost is dominated…

cs.DC2025

Striking the Right Balance between Compute and Copy: Improving LLM Inferencing Under Speculative Decoding

Arun Ramachandran, Ramaswamy Govindarajan, Murali Annavaram +4

With the skyrocketing costs of GPUs and their virtual instances in the cloud, there is a significant desire to use CPUs for large language model (LLM) inference. KV cache update, o…

cs.CL2025

DEL: Context-Aware Dynamic Exit Layer for Efficient Self-Speculative Decoding

Hossein Entezari Zarch, Lei Gao, Chaoyi Jiang +1

Speculative Decoding (SD) is a widely used approach to accelerate the inference of large language models (LLMs) without reducing generation quality. It operates by first using a co…

cs.LG2025

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

Chaoyi Jiang, Lei Gao, Hossein Entezari Zarch +1

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 activati…