Ensigncode delivers CUDA software development as a complete, scoped project rather than staff augmentation: we own kernel design, profiling-driven optimization, and production integration from a defined starting point to a measured, delivered result. Every project follows a measurement-first process, baseline, profile, implement, validate, and re-benchmark, so the delivered speedup is proven, not assumed.
Some GPU performance problems aren’t a staffing gap, they’re a defined piece of engineering work with a clear start and end point. Ensigncode’s CUDA software development services deliver exactly that: a scoped project with a fixed deliverable, built by engineers who profile first, implement second, and hand over a measured benchmark report alongside the code.
Need ongoing capacity instead of a fixed-scope project? See Hire CUDA Engineers for placements or CUDA Developers for Hire for flexible, specialty-matched engagement. All three build on the same CUDA engineering foundation.
What We Deliver
A CUDA software development engagement produces working, tested, production-ready code, not a report or a recommendation.
- Custom CUDA kernels for GEMM, SpMM, and other compute-heavy operations
- Stable C++ APIs with Python bindings (PyBind11, PyTorch extensions)
- Integrated inference pipelines with batching and scheduling tuned for your traffic
- A benchmark report showing baseline versus delivered performance
- Deployment-ready packaging, build configuration, and correctness tests
Our Development Process
The same measurement-first standard applies regardless of project size: establish a baseline, change one variable, benchmark, and report reproducible results.
- Scoping: define the workload, target hardware, and success metric upfront
- Baseline profiling with Nsight Systems and Nsight Compute before any code changes
- Implementation: kernel design, memory optimization, or integration work as scoped
- Numerical validation to confirm correctness and precision haven't regressed
- Final benchmarking against the original baseline, delivered with the code
Project Types We Take On
From a single optimized kernel to a full inference pipeline rebuild.
- Custom kernel development for operations library defaults don't cover well
- End-to-end GPU inference pipeline design and implementation
- C++/Python integration layers exposing optimized GPU code to your application
- Multi-GPU and rack-scale tuning for H100, B200, and GB200 NVL72 systems
- Legacy CUDA codebase modernization and performance recovery
Why Run This as a Project, Not a Staff Hire
- Fixed scope and defined deliverable, not an open-ended hourly engagement
- A measured benchmark report delivered alongside the code
- No ramp-up time lost to onboarding a new hire into your codebase
- Access to all six GPU specialist disciplines within one engagement
- Full IP ownership of everything delivered, under NDA from day one