Support in Model Optimisation: Pruning, Quantisation, and Distillation
Opis
This service provides technical support for improving AI model inference efficiency and reducing computational footprint through structured pruning, quantization, and knowledge distillation techniques. It ensures optimal use of HPC-AI resources by integrating these optimization methods into model development and deployment workflows. The service covers the full optimization pipeline: from baseline model profiling and bottleneck identification, through the application of compression techniques, to validation and deployment of lightweight, high-performance models suitable for constrained and production-grade environments. It enables users to achieve significant reductions in model size, memory usage, and inference latency while preserving task-specific accuracy within acceptable thresholds. The service applies structured and unstructured pruning methods to remove redundant parameters and reduce model complexity. Quantization techniques include post-training quantization (PTQ) using INT8, INT4, FP8, and FP4 formats, as well as quantization-aware training (QAT) for preserving accuracy under aggressive compression. Knowledge distillation workflows transfer capabilities from large teacher models to compact student architectures. Tooling includes NVIDIA TensorRT for optimized inference, PyTorch quantization APIs, ONNX Runtime, Hugging Face Optimum, and framework-specific pruning utilities like the NVIDIA Minitron approach and the NVIDIA Puzzle framework. Profiling and validation are performed on target GPU hardware to ensure optimization results are representative of production deployment conditions.
Pričakovani rezultati
Through bi-weekly consultations, clients co-develop optimized model variants with documented compression ratios, inference speedup measurements, and accuracy-versus-efficiency trade-off analysis. The service enables deployment of AI models on resource-constrained environments, edge devices, or production serving infrastructure with reduced compute costs and improved throughput while maintaining acceptable accuracy.
Kartica storitve
| Kategorija | User support |
| Domena | Model Optimisation, Efficient AI Deployment |
| Vertikala | Cross-domain |
| Ciljni uporabniki | SMEs, Startups, Research Institutions, Industrial R&D Teams |
| Tip storitve | Support, Consultations |
| Rezultat | Optimized model artifacts, compression ratio and accuracy reports, inference benchmarking results, deployment configuration files, and technical documentation. |
| Zahteve | Users should provide a trained baseline model, representative evaluation datasets, and target deployment constraints (latency, memory, hardware platform). Access to HPC-AI GPU infrastructure is provided for profiling and optimization. Familiarity with PyTorch, and specifically the model architecture and training pipeline, is helpful for efficient collaboration. |
| Omejitve | |
| Na voljo od | Q3/2026 |
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| Slika | |
| Oznaka slike |