jeejeelee/vllm
Jun 2026 – Jul 2026
2 Months active
Languages Used
No languages
Technical Skills
Backend DevelopmentCUDAPerformance OptimizationPyTorchPythonbackend
Cheng Jiang
PROFILE
Cheng Jiang
Contributed to the jeejeelee/vllm repository by developing and integrating high-performance backends for FP8 inference on NVIDIA Hopper GPUs, focusing on modularity and extensibility. Leveraged Python, CUDA, and PyTorch to implement expert-based MoE and attention backends, enabling FP8 quantization and bfloat16 cache support for improved throughput and reduced latency. Enhanced the attention backend with Multi-Token Prediction and dynamic scheduled attention, optimizing task scheduling and hardware utilization via the HPC library. The work emphasized maintainable architecture, seamless integration with model execution and attention registries, and readiness for production-grade deployment in high-throughput, next-generation accelerator environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
1,600
Activity Months2
Your Network
1457 peopleWork History
July 2026
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for jeejeelee/vllm: Implemented HPC_ATTN backend enhancements enabling Multi-Token Prediction (MTP) and dynamic scheduled attention, with FP8/bfloat16 improvements. The update adds MTP support, dynamic task scheduling via the hpc library, and improved FP8 quantization handling along with bfloat16 cache type support, enabling more efficient decoding for multi-token requests and better hardware utilization. Commit referenced: 95a248faed6781360eedcd37d6132a23f2c787da (Attention Backend] HPC_ATTN backend support mtp and dynamic scheduled attention (#47433)).
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for jeejeelee/vllm: Implemented HPC_ATTN backend enhancements enabling Multi-Token Prediction (MTP) and dynamic scheduled attention, with FP8/bfloat16 improvements. The update adds MTP support, dynamic task scheduling via the hpc library, and improved FP8 quantization handling along with bfloat16 cache type support, enabling more efficient decoding for multi-token requests and better hardware utilization. Commit referenced: 95a248faed6781360eedcd37d6132a23f2c787da (Attention Backend] HPC_ATTN backend support mtp and dynamic scheduled attention (#47433)).
July 2026
June 2026
2 Commits • 2 Features
Jun 1, 2026June 2026 monthly summary for jeejeelee/vllm: Delivered two HPC-Ops backends enabling FP8 inference on NVIDIA Hopper GPUs and FP8 KV-cache quantization, with modular design and integration into core components such as the model executor and attention registry. These gains improve throughput, reduce latency, and set the stage for production-grade FP8 inference on next-gen accelerators.
June 2026
2 Commits • 2 Features
Jun 1, 2026June 2026 monthly summary for jeejeelee/vllm: Delivered two HPC-Ops backends enabling FP8 inference on NVIDIA Hopper GPUs and FP8 KV-cache quantization, with modular design and integration into core components such as the model executor and attention registry. These gains improve throughput, reduce latency, and set the stage for production-grade FP8 inference on next-gen accelerators.
Activity
Loading activity data...
Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture93.4%
Performance100.0%
AI Usage73.4%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
Backend DevelopmentCUDAHPCPerformance OptimizationPyTorchPythonbackendcudahpcpythonpytorch
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline