You will get real-time CV and GenAI inference made measurably faster on the same GPU
Project details
Most of the time the model is not the problem. CPU-side preprocessing, synchronous copies, a batch size nobody chose deliberately. That is where the milliseconds usually are, and compiling the model does nothing about any of it.
So this is ordered by where the time actually turns out to be: measure, fix the pipeline, then convert the graph if there is still something left worth taking. Starter is the measurement on its own and stops before implementation.
If your model is a poor candidate for this, message me before ordering and I will tell you instead of taking the job.
So this is ordered by where the time actually turns out to be: measure, fix the pipeline, then convert the graph if there is still something left worth taking. Starter is the measurement on its own and stops before implementation.
If your model is a poor candidate for this, message me before ordering and I will tell you instead of taking the job.
AI Development Type
Deep LearningAI Tools
NVIDIA AI Platform, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$500
|
Standard
$2,500
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 days |
AI Model Integration | - | - | |
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
About Chirag
Senior Computer Vision Engineer | Real-Time GPU Inference
Gurugram, India - 11:14 am local time
Three years of taking real-time CV and GenAI systems that were too slow or too expensive and making them not that. Lipsync latency down from 6 seconds to 1.2. Body-mesh detection 7x faster. One infra rebuild took six figures a year off a GPU bill and still held 99% uptime.
The actual work is unglamorous. Profile until the real bottleneck shows up, build a custom TensorRT engine, move preprocessing onto the GPU, kill the sync I/O, fight with encoder settings nobody wants to read about.
What you get is honest numbers. I'll tell you which fix is 5x and which is 5%, and I won't promise you real-time on hardware that can't do it. Tell me what your pipeline does and where it hurts. I'll tell you if I'm useful.
Steps for completing your project
After purchasing the project, send requirements so Chirag can start the project.
Delivery time starts when Chirag receives requirements from you.
Chirag works on your project following the steps below.
Revisions may occur after the delivery date.
Review
Review the model, sample input and target hardware. Confirm scope or flag blockers within 24 hours
Profiling
Baseline profiling run: per-stage latency, GPU utilization, transfer and host-side overhead