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I will optimize and benchmark your ai model on real nvidia jetson hardware
Fawad Sarim
About this gig
Your model works on your laptop. Will it work on the device?
Most AI performance problems aren't model problems, they're measurement problems. Teams size hardware from FLOPs or spec-sheet TOPS, and real silicon disagrees, sometimes by more than a factor of two on configurations I have measured.
I test your model on real NVIDIA Jetson hardware and tell you what it does.
WHAT YOU GET
Real measurements on physical hardware, not estimates
Worst-case latency (p99), not just averages
Per-stage breakdown showing where the time goes
A written report with the numbers and what they mean
Honest answers, including "this won't work on that board"
WHO THIS IS FOR
You're choosing hardware and need to know if your model fits
Your model is slower on-device than expected and nobody knows why
You need TensorRT conversion or quantization done right
You want an independent check on a vendor's claim
WHY ME
MS in AI Engineering, with research in efficient vision model deployment on embedded hardware. Strict protocol: locked clocks, warm-up discarded, medians over hundreds of runs, thermal behaviour recorded. Most benchmarks are none of those things.
Message me before ordering to confirm fit.
Get to know Fawad Sarim
Fawad Sarim
Computer Vision, Edge AI Engineer, Jetson, RK3588 Deployment
- FromPakistan
- Member sinceMay 2026
Languages
Urdu, English
I make AI models run on real hardware, and I prove it with measurements rather than estimates.
Engineer with an MS in Artificial Intelligence Engineering and active research in efficient vision model deployment on embedded hardware. I solve the problem teams hit after the model works: making it meet a real latency, power, or thermal budget on the device it ships on. I test on physical NVIDIA Jetson hardware, so you get numbers from silicon rather than from a spec sheet.
If something will not work on your target, you will hear it early. That answer is often worth more than the work.
FAQ
I don't have a Jetson. Can you still help?
Yes, that's the point. I test on my own hardware and send you the results, so you can decide before buying anything.
Which Jetson do you test on?
Jetson Orin Nano Super. If you're targeting a different board, message me first, some results transfer, some don't, and I'll tell you which.
What if my model can't hit the target?
Then I'll tell you that, with the numbers showing why and what would need to change. That answer is often worth more than the optimization.
Is my model confidential?
Yes. I don't share, reuse, or retain client models beyond the work. Happy to sign an NDA.
Do your latency numbers hold up in production?
Yes. A 3-minute bench test is fiction. I run your model under sustained load to measure time-to-first-throttle. You get the worst-case (p99) latency for a device that is hot, because that is what your product will actually experience.
The spec sheet says this board has 60 TOPS. Why is my model missing deadlines?
TOPS is a theoretical peak calculated for large batches. Real-time edge vision operates at Batch 1, where memory bandwidth and fixed kernel overhead dominate arithmetic. I measure actual milliseconds on silicon, not paper FLOPs.
Will quantization (FP16/INT8) destroy my model's accuracy?
Not when calibrated correctly. I use a dedicated RTX 4090 workstation to perform rigorous entropy calibration on your sample data before moving the engine to the Jetson. This ensures maximum inference speed on the edge with near-zero accuracy loss.
My model is fast in PyTorch but my system FPS is low. Can you fix it?
The model usually isn't the bottleneck, your input pipeline is. Preprocessing (resizing, color conversion) and memory copies often cost more than inference. I profile your end-to-end system to find where the time actually goes.
What if my model has custom layers that TensorRT doesn't support?
Native TensorRT operations are always prioritized for maximum speed. If a native op isn't available, I handle it by either writing custom C++ plugins or structuring the pipeline to safely fall back to ONNX Runtime or PyTorch on the Jetson.
