flashinfer-ai/flashinfer
Apr 2026 – Jun 2026
3 Months active
Languages Used
CUDAPythonC++
Technical Skills
Deep LearningGPU ProgrammingPyTorchCUDAData StructuresMachine Learning
Duncan Moss
PROFILE
Duncan Moss
Worked on the flashinfer-ai/flashinfer repository to enhance deep learning inference capabilities, focusing on GPU programming and kernel optimization using CUDA, C++, and Python. Delivered support for large head dimensions in attention kernels, expanded test coverage for reliability, and implemented production-ready CuTe DSL kernels for recurrent KDA on Blackwell hardware. Addressed compatibility issues with FMHA artifacts and improved kernel selection logic for scalable, low-latency inference. Resolved decoding bugs for GQA with large head dimensions and collaborated on refining softmax scaling and kernel registration. Emphasized robust validation, benchmarking, and cross-hardware compatibility to ensure stable, high-performance deployment across diverse model configurations.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
2
Commits
4
Features
2
Lines of code
5,485
Activity Months3
Your Network
1960 peopleSame Organization
@nvidia.com1786
Work History
June 2026
1 Commits
Jun 1, 2026June 2026 performance-focused update for flashinfer: resolved GQA decoding issues with large head dimensions, improved kernel selection, and broadened test coverage; delivered reliable, scalable inference for headDim=512 configurations; co-authored by Duncan Moss.
1 Commits
Jun 1, 2026June 2026 performance-focused update for flashinfer: resolved GQA decoding issues with large head dimensions, improved kernel selection, and broadened test coverage; delivered reliable, scalable inference for headDim=512 configurations; co-authored by Duncan Moss.
June 2026
May 2026
2 Commits • 1 Features
May 1, 2026May 2026: Strengthened decoder performance and KDA capabilities for flashinfer-ai/flashinfer. Delivered production-ready CuTe DSL kernels for recurrent KDA on SM100 (Blackwell), fixed critical FMHA ABI/cubin alignment for newer hardware, and expanded test coverage and benchmarking to validate performance and stability. These changes improve latency, hardware compatibility, and scalability for deployment.
May 2026
2 Commits • 1 Features
May 1, 2026May 2026: Strengthened decoder performance and KDA capabilities for flashinfer-ai/flashinfer. Delivered production-ready CuTe DSL kernels for recurrent KDA on SM100 (Blackwell), fixed critical FMHA ABI/cubin alignment for newer hardware, and expanded test coverage and benchmarking to validate performance and stability. These changes improve latency, hardware compatibility, and scalability for deployment.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary focusing on feature expansion for TRTLLM attention kernels, validation coverage, and preparation for future kernel upgrades. Delivered larger head_dim support and strengthened test suites to improve enterprise reliability and performance, enabling broader model configurations and safer deployments.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary focusing on feature expansion for TRTLLM attention kernels, validation coverage, and preparation for future kernel upgrades. Delivered larger head_dim support and strengthened test suites to improve enterprise reliability and performance, enabling broader model configurations and safer deployments.
April 2026
Activity
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture85.0%
Performance85.0%
AI Usage55.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
CUDAData StructuresDeep LearningGPU ProgrammingMachine LearningPyTorch
Repositories Contributed To
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