microsoft/onnxruntime-genai
Jul 2025 – Sep 2025
2 Months active
Languages Used
C++PythonCUDAMarkdown
Technical Skills
C++ developmentCUDAGPU programmingPerformance optimizationPython scriptingbenchmarking
Gaurav Garg
PROFILE
Gaurav Garg
Gaurav Garg contributed to microsoft/onnxruntime-genai by engineering high-throughput inference improvements and enhancing benchmarking reliability for GenAI workloads. He optimized GPU-based sampling and tuned batch size and sequence length profiles, leveraging C++ and CUDA to increase inference efficiency and throughput. His work included refining CUDA kernel logic for top-k sampling, improving GPU utilization, and reducing latency. Gaurav also strengthened the stability and deployment readiness of the TRT-RTX Execution Provider, addressing regression issues and optimizing KV cache re-computation. Through Python scripting and rigorous benchmarking, he delivered measurable improvements in performance, reliability, and validation pipelines, demonstrating strong depth in GPU programming and optimization.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
4
Lines of code
230
Activity Months2
Your Network
1419 people
Work History
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary focusing on performance and stability improvements across ONNX Runtime GenAI and TRT-RTX EP, delivering measurable business value through faster inference, increased reliability, and broader deployment readiness. Key work includes CUDA kernel optimizations for top-k sampling, TRT-RTX EP stability and capability enhancements, and test hygiene improvements that reduce compile-time failures. These efforts improved GPU utilization, reduced latency for GenAI workloads, and strengthened validation pipelines.
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary focusing on performance and stability improvements across ONNX Runtime GenAI and TRT-RTX EP, delivering measurable business value through faster inference, increased reliability, and broader deployment readiness. Key work includes CUDA kernel optimizations for top-k sampling, TRT-RTX EP stability and capability enhancements, and test hygiene improvements that reduce compile-time failures. These efforts improved GPU utilization, reduced latency for GenAI workloads, and strengthened validation pipelines.
September 2025
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for microsoft/onnxruntime-genai. Focused on delivering high-throughput inference improvements for TRT-RTX and strengthening benchmarking reliability for CUDA Execution Provider (CUP). The work aligns with GenAI workloads, accelerating real-time capabilities and enabling better performance attribution for optimization efforts.
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 performance summary for microsoft/onnxruntime-genai. Focused on delivering high-throughput inference improvements for TRT-RTX and strengthening benchmarking reliability for CUDA Execution Provider (CUP). The work aligns with GenAI workloads, accelerating real-time capabilities and enabling better performance attribution for optimization efforts.
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Quality Metrics
Correctness97.6%
Maintainability87.4%
Architecture90.0%
Performance95.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
C++CUDAMarkdownPython
Technical Skills
C++C++ developmentCUDACUDA programmingDeep LearningGPU optimizationGPU programmingPerformance optimizationPerformance tuningPython scriptingbenchmarkingdocumentationhardware accelerationmachine learningmodel optimization
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
CodeLinaro/onnxruntime
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++
Technical Skills
C++ developmentunit testing