Your PyTorch model delivers 20 FPS on an Intel Xeon, but the client demands 60 FPS on an edge device with a 5 W power budget. A typical scenario: GPU instances are expensive, while the NPU on Intel Core Ultra sits idle. OpenVINO solves this: conversion, INT8 quantization, and deployment on NPU yield 84 FPS at 4.2 W. We use Model Optimizer to convert PyTorch, TensorFlow, and ONNX into a unified IR format. Post-Training Optimization Tool (POT) performs INT8 calibration with accuracy control. For YOLOv8n (2.3M params) on a Core i5-14500, latency drops from 35 ms to 12 ms after quantization. Infrastructure savings reach $5,000–10,000 per year compared to a GPU rack.
We integrate ML models into the Intel ecosystem: CPU (Xeon, Core), NPU (Core Ultra), VPU (Movidius, including Neural Compute Stick). Unlike TensorRT, OpenVINO is not just a runtime but a full optimization pipeline. Typical scenario: a model runs on NVIDIA, the client wants to move to Intel edge. We convert, quantize to INT8, and deploy into production with OpenVINO Model Server. Infrastructure costs drop 2–3 times due to reduced latency and power consumption.
If your model runs slowly on Intel or is incompatible with target hardware, we accelerate it 2–3 times and adapt it for NPU/VPU. With 30+ projects, we have experience with YOLO, ResNet, BERT, and custom architectures. We guarantee at least 40% latency reduction or your money back.
What Problems Does OpenVINO Solve?
High latency on CPU. A PyTorch model gives 20–50 FPS on Xeon. After conversion to IR and INT8 quantization, it delivers 80–120 FPS. Acceleration of 2–3 times.
Format incompatibility. TF SavedModel, PyTorch, ONNX — Model Optimizer turns them into a single IR. No manual graph edits.
Power consumption. NPU on Core Ultra consumes <5 W instead of 15 W on GPU. Ideal for always-on systems.
Why OpenVINO Over ONNX Runtime on Intel?
ONNX Runtime uses generic kernels, not optimized for specific Intel hardware types. OpenVINO includes runtime caching, INT8 calibration, and NPU/VPU support. On Xeon with VNNI (AVX-512), there is up to 30% gain over ORT. The license is free, and the OpenVINO documentation recommends it for edge solutions.
How We Convert Models: Step-by-Step Guide
- Analyze the source model and target hardware.
- Export to ONNX (if PyTorch) or import directly via Model Optimizer.
- Quantize: run
pot -c config.jsonon a calibration dataset. The INT8 model is 4x lighter, latency drops 2–3x. - Test accuracy (mAP, F1) — we allow no more than 1% drop.
- Deploy: OpenVINO Model Server with gRPC or embed mode.
When testing YOLOv8n on Intel Core i5-14500 (FP32: 28 FPS), after INT8 calibration we got 84 FPS, latency 12 ms. Deployment on NPU Core Ultra reduced power consumption to 4.2 W. Savings on electricity — thousands of dollars per year compared to a GPU rack.
What INT8 Quantization Delivers?
Weight reduction of 75% and speedup of 2–3 times. With proper calibration, accuracy drops no more than 0.5–1%. For detection, classification, NLP — standard practice. We use POT (Post-training Optimization Tool) or NNCF for finer control.
What Models Do We Convert?
The table below shows typical scenarios.
| Source Framework | IR Conversion | Quantization | Recommended Path |
|---|---|---|---|
| TensorFlow | Model Optimizer + tf2onnx | POT/NNCF | TF Hub -> ONNX -> IR |
| PyTorch | torch.onnx + mo | POT/NNCF | ONNX -> IR |
| ONNX | mo --input_model | POT | Direct conversion |
| PaddlePaddle | mo --input_model | POT | Via ONNX or direct import |
Performance Table
| Device | Model | Precision | Latency | Power Consumption |
|---|---|---|---|---|
| Core i5-14500 (CPU) | ResNet-50 | FP32 | 4.2 ms | 65 W |
| Core i5-14500 (CPU) | ResNet-50 | INT8 | 1.8 ms | 65 W |
| Core Ultra 7 155H (NPU) | ResNet-50 | INT8 | 2.1 ms | 4.2 W |
| Xeon Platinum 8358 (CPU) | BERT-base | FP32 | 7.5 ms | 250 W |
| Xeon Platinum 8358 (CPU) | BERT-base | INT8 | 3.2 ms | 250 W |
What Is Included in the Service
- Model audit and target hardware analysis.
- Conversion to OpenVINO IR (FP32, FP16, INT8).
- INT8 calibration with accuracy profiling.
- Integration with OpenVINO Model Server or embedded runtime.
- Load testing (latency p99, throughput).
- Deployment and configuration documentation.
- 30 days of support after deployment.
Timelines and Cost
Timelines: from 1 to 3 weeks depending on model complexity. Exact estimate after analysis. We guarantee at least 40% latency reduction or your money back. Average savings from optimization — from $2,000 to $10,000 per year on infrastructure.
Order a free audit of your model — we will assess the optimization potential in 1 business day. Contact us for a project consultation. Our engineers are Intel certified on OpenVINO.