Model Forge
CVEDIA's proprietary end-to-end model lifecycle and deployment system, built to ensure that AI models don’t just work — they work everywhere they are deployed.
Bridging Data Science and Deployment
Model Forge bridges the gap between advanced data science, real-world hardware constraints, and continuous performance improvement. All without adding complexity for integrators or end users.
- — Automatic optimization for any target hardware
- — One canonical model per capability, improvements benefit everyone
- — Consent-based active learning for continuous improvement
- — Version-controlled, non-breaking updates
Modern AI doesn't fail in the lab, it fails in deployment. Model Forge is CVEDIA's answer to this reality.
One Model Definition.
Every Target Optimized
Model Forge is a neural network compiler that transforms a single model architecture into hardware-specific optimized artifacts. It handles quantization, kernel fusion, memory layout optimization, and instruction scheduling, automatically tuned for each target's constraints.
INT8 Automatic quantization with calibration, no manual tuning required FUSE Layer fusion and kernel optimization per-backend TILE Memory tiling for cache efficiency on constrained devices BENCH Automated accuracy and latency validation per target Supported Inference Targets
Auto-detect and deploy. Runtime selects optimal backend automatically.
Technical Capabilities
Quantization
Post-training quantization with INT8/FP16, per-channel and per-tensor calibration, mixed-precision layer selection, accuracy validation against FP32 baseline, and automatic fallback for sensitive layers.
Optimization
Conv-BN-ReLU fusion, attention kernel optimization, memory layout transformation (NCHW↔NHWC), constant folding and dead code elimination, and dynamic shape support where available.
Validation
Automated benchmark suite per target, latency P50/P95/P99 profiling, mAP regression testing, memory usage tracking, and CI/CD integration support.
Continuous Deployment Pipeline
One canonical model. Improvements ship to all targets automatically.
Train
Single model trained on synthetic + real data
Compile
Model Forge generates optimized artifacts
Validate
Automated testing on all target hardware
Deploy
Runtime auto-selects correct variant
Learn
Feedback improves next iteration
// Initialize runtime - hardware auto-detected
auto runtime = cvedia::Runtime();
// Load model - correct variant selected
auto model = runtime.loadModel("person_vehicle");
// Run inference - same API everywhere
auto detections = model.infer(frame);
// That's it. Works on GPU, CPU, NPU.Works on any device. No extra configuration
Integrators write against a single API. Model Forge handles device detection, model selection, and memory management. Deploy to a datacenter GPU or a $50 edge device with the same code path.
See Model Forge in Action
Run our models on your hardware. We'll show you the numbers.