I migrate slow, bottlenecked Python AI prototypes into high-speed, zero-copy C++/CUDA production architectures. Hit your FPS targets directly on the edge.
A recent benchmark of a YOLOv8 object detection pipeline running on live video feeds. The difference between standard Python and optimized C++.
The Python Prototype
Max Concurrent Streams30
CPU Usage100% (Choked)
VRAM Consumption9.1 GB
PRODUCTION GRADE
The C++/CUDA Pipeline
Max Concurrent Streams200+
CPU UsageNear 0%
VRAM Consumption2.7 GB
About the Architect
I am a Computer Vision Performance Architect with over 7 years of specialized experience deploying production-grade deep learning systems across the DACH region.
Holding an M.Sc. in Automotive Engineering and a deep background in hardware integration, I don't just write code; I architect systems that respect physical hardware limits. Operating as Ingenieurbüro Anwar, I partner with AI startups and industrial manufacturers to bridge the gap between Python research prototypes and robust, hardware-accelerated C++/CUDA reality.
M.Sc. Automotive Engineering
7+ Years DACH Experience
Saqib Anwar
Ingenieurbüro Anwar
Optimization Services
Rigorous, data-backed performance pipelines engineered for mission-critical computer vision systems.
Phase 01 // Diagnostic
Inference Pipeline Audit
€1,800 • 72-Hour Turnaround
Before refactoring production code, we establish an empirical baseline. Utilizing NVIDIA Nsight Systems and Compute, I profile your entire inference stack to map out memory transfer overheads, CPU-GPU serialization gaps, and unoptimized kernels.
✓ Comprehensive GPU/CPU bottleneck mapping
✓ VRAM allocation and unified memory analysis
✓ Step-by-step C++/CUDA execution roadmap
*Fee is fully credited against subsequent implementation phases.
Production Systems
Phase 02 // Execution
Custom Pipeline Optimization
Custom Quoted • Typically €12K - €25K
Full architectural migration of bottlenecked prototypes into robust, hardware-accelerated native infrastructure. Engineered specifically for your deployment envelope, from edge Jetson nodes to dense multi-GPU server architectures.
✓ Zero-copy unified memory & custom CUDA kernels
✓ TensorRT engine compilation & INT8 quantization
✓ Multi-stream hardware media decoding (NVDEC/libav)
Scoped directly from the execution roadmap established in Phase 01.
