4 papers · 1 filter
LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization
Yi Pan, Haocheng Xi, Kan Zhu +10
Recent LLMs increasingly adopt hybrid designs that replace standard attention with linear attention, such as Gated DeltaNet (GDN) and Kimi Delta Attention (KDA). Although they comp…
HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval
Omin Kwon, Doyeon Kim, Jongseok Park +3
The KV cache dominates GPU memory in long-context LLM serving, crowding out batch capacity and leaving GPU compute idle. Offloading the cache to CPU DRAM restores capacity, but the…
Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution
Jongseok Park, Sunga Kim, Zhenyu Gu +2
Mixture of Experts (MoE) architecture has become the standard for state-of-the-art large language models, owing to its computational efficiency through sparse expert activation. Ho…
Accelerating Large-Scale Reasoning Model Inference with Sparse Self-Speculative Decoding
Yilong Zhao, Jiaming Tang, Kan Zhu +10
Reasoning language models have demonstrated remarkable capabilities on challenging tasks by generating elaborate chain-of-thought (CoT) solutions. However, such lengthy generation…