11 papers
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…
Qrita: High-performance Top-k and Top-p using Pivot-based Truncation and Selection
Jongseok Park, Sunga Kim, Alvin Cheung +1
Despite their importance in model sampling, efficient implementation of Top-k and Top-p algorithms for large vocabularies remains a significant challenge. Existing approaches often…
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…
Unleashing Scalable Context Parallelism for Foundation Models Pre-Training via FCP
Yilong Zhao, Xiaonan Nie, Kan Zhu +6
Context parallelism (CP) has been widely adopted to support the growing context length in foundation model pretraining. However, existing designs fail to handle the large variation…
Speculative Decoding: Performance or Illusion?
Xiaoxuan Liu, Jiaxiang Yu, Jongseok Park +2
Speculative decoding (SD) has become a popular technique to accelerate Large Language Model (LLM) inference, yet its real-world effectiveness remains unclear as prior evaluations r…
: Faster Test-Time Scaling through Speculative Drafts
Mert Cemri, Nived Rajaraman, Rishabh Tiwari +6
Scaling test-time compute has driven the recent advances in the reasoning capabilities of large language models (LLMs), typically by allocating additional computation for more thor…