NVIDIA/TensorRT-LLM
Nov 2025 – Mar 2026
3 Months active
Languages Used
PythonCUDA
Technical Skills
NVIDIA TensorRTPyTorchdeep learningmachine learningCUDA programmingGPU optimization
Tri Dao
PROFILE
Tri Dao
Worked on NVIDIA/TensorRT-LLM and confident-ai/deepeval, delivering features and reliability improvements for deep learning model deployment. Developed Glm4MoeForCausalLM model support and implemented Deepseekv3 mixed quantization, enabling flexible quantization strategies and mixture-of-experts inference. Enhanced CUDA kernel synchronization by refactoring grid dependency control to use CUDA APIs, improving maintainability and performance. Addressed memory management and reliability by adding host cache offloading and fixing issues in Dynamic Sparse Attention and PDL routing. Contributed targeted bug fixes in Python and CUDA, such as correcting variable names in deepeval, which stabilized benchmarking workflows and improved CI reliability for downstream evaluation tasks.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
3
Lines of code
1,419
Activity Months4
Your Network
350 peopleShared Repositories
350
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for NVIDIA/TensorRT-LLM focused on delivering high-value features and reliability improvements to enhance deployment readiness, configurability, and inference performance.
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for NVIDIA/TensorRT-LLM focused on delivering high-value features and reliability improvements to enhance deployment readiness, configurability, and inference performance.
March 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12: Focused on stabilizing and accelerating the NVIDIA/TensorRT-LLM path for dsv3. Delivered CUDA kernel synchronization improvements and resolved a PDL-related MOE issue, enhancing performance, reliability, and maintainability of the inference pipeline by refactoring synchronization to use CUDA APIs instead of inline assembly.
December 2025
1 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12: Focused on stabilizing and accelerating the NVIDIA/TensorRT-LLM path for dsv3. Delivered CUDA kernel synchronization improvements and resolved a PDL-related MOE issue, enhancing performance, reliability, and maintainability of the inference pipeline by refactoring synchronization to use CUDA APIs instead of inline assembly.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for NVIDIA/TensorRT-LLM. Key delivery: Glm4MoeForCausalLM Model Support, enabling causal language modeling with mixture-of-experts capabilities in the library. Implemented in commit fc088e642c08e0c2b3cc49e242703a19ebc31bc6, advancing the platform's model deployment options.
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for NVIDIA/TensorRT-LLM. Key delivery: Glm4MoeForCausalLM Model Support, enabling causal language modeling with mixture-of-experts capabilities in the library. Implemented in commit fc088e642c08e0c2b3cc49e242703a19ebc31bc6, advancing the platform's model deployment options.
November 2025
May 2025
1 Commits
May 1, 2025May 2025 monthly summary for confident-ai/deepeval: Delivered a targeted bug fix in the MMLU benchmark to ensure stable prompt generation and evaluation. Corrected a dataset variable name typo to prevent an undefined-variable runtime error, improving reliability of evaluation prompts and data integrity. The change is tracked in commit 7382043108f540a701359c38c32f53e433400d59, with impact on downstream benchmarking runs and CI stability.
May 2025
1 Commits
May 1, 2025May 2025 monthly summary for confident-ai/deepeval: Delivered a targeted bug fix in the MMLU benchmark to ensure stable prompt generation and evaluation. Corrected a dataset variable name typo to prevent an undefined-variable runtime error, improving reliability of evaluation prompts and data integrity. The change is tracked in commit 7382043108f540a701359c38c32f53e433400d59, with impact on downstream benchmarking runs and CI stability.
Activity
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Quality Metrics
Correctness92.0%
Maintainability84.0%
Architecture88.0%
Performance88.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
CUDAPython
Technical Skills
Bug FixCUDA programmingDeep LearningGPU optimizationGPU programmingMachine LearningModel OptimizationNVIDIA TensorRTParallel computingPyTorchQuantizationRefactoringdeep learningmachine learningmemory management
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
confident-ai/deepeval
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Bug FixRefactoring