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Duncan Moss

PROFILE

Duncan Moss

Over seven months, contributed advanced GPU and deep learning optimizations to bytedance-iaas/vllm and flashinfer-ai/flashinfer, focusing on Mixture-of-Experts (MoE) and quantization. Developed and integrated fused CUTLASS kernels for FP8 and MXFP4 data types, enabling higher throughput and lower latency on SM90 and SM100 architectures. Enhanced packaging reliability for FlashInfer across Python package managers and improved numerical stability in top-p sampling. Leveraged C++, CUDA, and Python to deliver robust backend features, including 512-head attention support and architecture-specific quantization libraries, while validating changes through comprehensive testing and cross-repo integration to ensure production readiness and deployment flexibility.

Overall Statistics

Feature vs Bugs

78%Features

Repository Contributions

12Total
Bugs
2
Commits
12
Features
7
Lines of code
22,561
Activity Months7

Work History

June 2026

1 Commits

Jun 1, 2026

June 2026 consolidated a critical stability improvement in the Top-P probability renormalization path for flashinfer. By refining the termination condition of the floating-point loop to stop when candidate bounds become adjacent FP32 values, we eliminated non-progress near numerical boundaries and improved correctness in small-vocab top-p sampling. The change reduces the risk of unstable sampling behavior in production and shores up generation quality across per-request top_p configurations. Validation included a diff-based review against upstream/main and targeted GPU top-p reference checks.

May 2026

1 Commits • 1 Features

May 1, 2026

May 2026 monthly summary for jeejeelee/vllm: Delivered a high-impact feature in the FlashInfer path that enables 512-head attention and FP8 KV cache optimizations. This expands model capacity and improves inference throughput by adjusting head_dim handling and KV cache logic for FP8 data. No major bugs fixed this month. Key commit: 552bbe6f4e6da23def2adf9fbc39c3ad0250f1e1.

November 2025

2 Commits • 2 Features

Nov 1, 2025

November 2025: Focused on strengthening packaging reliability and delivering performance-oriented features across FlashInfer repos. Delivered cross-package-manager JIT include-detection and FP8 blockscale support on SM90, enabling broader deployment and faster inference.

October 2025

1 Commits • 1 Features

Oct 1, 2025

October 2025 monthly summary for flashinfer-ai/flashinfer: Key feature delivered is FP8 Block Scaling MoE support for SM90 (Hopper) using fused Cutlass operations. This work introduces FP8 kernel definitions and implementations, leveraging Tensor Memory Access (TMA) and Warp Group Matrix Multiply Accumulate (WGMMA) for optimized FP8 performance. It includes kernel logic for FP8 data handling, shared memory management, and integration with the FP8 Block Scaling MoE pathway. The change is tracked in commit 8276d03c368e49b25736a97d29d6d70e089be985 (feat:enable fp8 blockscale moe for fused cultass for sm90 (#1819)).

September 2025

1 Commits • 1 Features

Sep 1, 2025

September 2025 — Repository: bytedance-iaas/vllm. Focused on delivering high-performance MXFP4 fused CUTLASS MoE kernel with testing and FlashInfer backend integration. Key outcomes include enabling MXFP4 fused MoE kernel on Blackwell (SM 10.0) and Hopper (SM 9.0) GPUs, introducing comprehensive tests, and integrating FlashInfer's CUTLASS backend to accelerate MoE workloads. No major bugs reported in scope for this period. Business impact: higher inference throughput for Mixture-of-Experts on next-gen GPUs, improved reliability via end-to-end tests, and smoother production readiness with FlashInfer integration. Technologies demonstrated: CUDA kernel development, CUTLASS, MXFP4 quantization, FlashInfer backend integration, MoE architectures, GPU performance testing, and test automation.

August 2025

2 Commits • 1 Features

Aug 1, 2025

In August 2025, I delivered focused MoE performance and robustness improvements for flashinfer, emphasizing cross-architecture optimization and safer MoE execution. Key work includes mixed-precision MoE kernel support across SM100 and SM90, with SwigluBias activation, plus robustness enhancements including OOB boundary checks in fused MoE and architecture-specific FP4 quantization library refactors for SM90/SM100 to improve compatibility and stability. These changes are designed to increase throughput for large MoE models, expand hardware support, and reduce risk in production deployments.

July 2025

4 Commits • 1 Features

Jul 1, 2025

July 2025 monthly summary for bytedance-iaas/vllm. Implemented GPU-accelerated GEMM improvements for SM100 with FP8, delivering performance gains and enabling smaller batch sizes. Also applied a stability fix to maintain compatibility with activation functions and input conditions by disabling the Cutlass Block Scaled Group GEMM in expert parallelism mode. Result: higher throughput on SM100 FP8 paths, lower latency, and more robust, maintainable execution across workflows. Technologies involved include CUTLASS, SM100, FP8, and group GEMM optimizations.

Activity

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Quality Metrics

Correctness91.6%
Maintainability85.0%
Architecture90.0%
Performance90.8%
AI Usage45.0%

Skills & Technologies

Programming Languages

C++CMakeCUDAPython

Technical Skills

Backend DevelopmentC++C++ developmentCUDACUDA ProgrammingCUDA programmingCUTLASSCUTLASS LibraryCutlass LibraryDeep LearningDeep Learning OptimizationFP8 Data TypeFlashInferGPU ComputingGPU Programming

Repositories Contributed To

3 repos

Overview of all repositories you've contributed to across your timeline

bytedance-iaas/vllm

Jul 2025 Sep 2025
2 Months active

Languages Used

C++CMakeCUDAPython

Technical Skills

CUDACUDA programmingDeep LearningGPU ProgrammingGPU programmingMachine Learning

flashinfer-ai/flashinfer

Aug 2025 Jun 2026
4 Months active

Languages Used

C++CUDAPython

Technical Skills

C++CUDACUDA ProgrammingCUTLASS LibraryDeep Learning OptimizationGPU Computing

jeejeelee/vllm

Nov 2025 May 2026
2 Months active

Languages Used

Python

Technical Skills

CUDAGPU programmingdeep learningmodel optimizationquantizationBackend Development