microsoft/onnxruntime
Jun 2025 – Jun 2025
1 Month active
Languages Used
C++
Technical Skills
C++ DevelopmentC++ developmentCUDADeep LearningGPU ProgrammingMachine Learning
Vishal Agarwal
PROFILE
Vishal Agarwal
Vishal Agarwal contributed to both ggml-org/llama.cpp and microsoft/onnxruntime, focusing on performance benchmarking and deployment optimization. In llama.cpp, he developed a context depth benchmarking feature by adding a -d flag to llama-bench, enabling more accurate and reproducible model performance comparisons across different context depths, and updated documentation to support cross-team adoption. For onnxruntime, he engineered weight-stripped engine loading for NVIDIA TensorRT RTX EP engines, reducing disk usage and supporting flexible deployment, while also fixing device ID checks to improve build stability. His work demonstrated strong proficiency in C++, CUDA, and command-line interface design, with thoughtful attention to maintainability.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
236
Activity Months2
Your Network
419 peopleWork History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 (2025-06) focused on optimizing NVIDIA TensorRT RTX EP workflows and hardening build stability in microsoft/onnxruntime. Key contributions delivered weight-stripped engine loading for NV TRT RTX EP engines under EP Context, reducing disk footprint and enabling dual weight-loading paths. Also fixed device ID checks in CUDA and TensorRT EP builds, improving device management and cross-provider compatibility. These changes enhance deployment flexibility, runtime efficiency, and CI stability, underscoring proficiency in CUDA, TensorRT, and ONNX Runtime engineering.
2 Commits • 1 Features
Jun 1, 2025June 2025 (2025-06) focused on optimizing NVIDIA TensorRT RTX EP workflows and hardening build stability in microsoft/onnxruntime. Key contributions delivered weight-stripped engine loading for NV TRT RTX EP engines under EP Context, reducing disk footprint and enabling dual weight-loading paths. Also fixed device ID checks in CUDA and TensorRT EP builds, improving device management and cross-provider compatibility. These changes enhance deployment flexibility, runtime efficiency, and CI stability, underscoring proficiency in CUDA, TensorRT, and ONNX Runtime engineering.
June 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for ggml-org/llama.cpp focusing on features delivered, impact, and skills demonstrated. The month centered on delivering a targeted benchmark capability and documenting it for cross-team reuse, with a clear line of sight to business value through improved benchmarking accuracy and resource-optimization insights.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for ggml-org/llama.cpp focusing on features delivered, impact, and skills demonstrated. The month centered on delivering a targeted benchmark capability and documenting it for cross-team reuse, with a clear line of sight to business value through improved benchmarking accuracy and resource-optimization insights.
Activity
Loading activity data...
Quality Metrics
Correctness93.4%
Maintainability80.0%
Architecture86.6%
Performance80.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
C++Markdown
Technical Skills
C++ DevelopmentC++ developmentCUDADeep LearningGPU ProgrammingMachine LearningSoftware DevelopmentTensorRTcommand-line interface designperformance benchmarking
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
ggml-org/llama.cpp
Apr 2025 – Apr 2025
1 Month active
Languages Used
C++Markdown
Technical Skills
C++ developmentcommand-line interface designperformance benchmarking