Songwei Liu
I build efficient LLM/AIGC inference systems for edge-cloud computing platforms.
Songwei Liu is an MLSys optimization expert in the Data-AML Heterogeneous Hardware team at ByteDance. He obtained his bachelor's degree from Huazhong University of Science & Technology, and his master's degree from Zhejiang University.
His research focuses on efficient model architecture design and foundation model training, algorithm/model optimization and software-hardware co-optimization, and inference optimization for multi-end heterogeneous platforms.
Efficient AIGC
Quantization/sparsity-driven software-hardware co-optimization, cache/MoE-token/resolution compression, and efficient foundation model training.
Efficient LLM
Quantized and sparse inference/training, speculative decoding, long-context acceleration, and deployment-oriented compression.
Heterogeneous Inference
Long-context inference systems, agentic workload serving, KVCache systems, and multi-end edge-cloud deployment.
At ByteDance, Songwei Liu leads a model optimization team that provides post-training optimization, algorithm/model optimization, and software-hardware co-optimization for Seedance, Seedream, and Volcengine open-source LLM/VLM models, substantially reducing cloud inference costs for these model families.
His academic work spans ICML, ICLR, ACL, AAAI, IJCNLP-AACL, ACM-MM, CVPRW, and Nature Communications, with a focus on practical efficiency methods that transfer from papers to production systems.
He is interested in academic cooperation around efficient AIGC/LLM systems, foundation model optimization, and software-hardware co-design. His team regularly recruits interns; interested candidates can apply through the ByteDance referral link or contact him by email.