Solution Overview
We compress heavy Large Language Models (LLMs) and computer vision networks by representing their weight matrices as low-rank tensor networks. This reduces parameters, memory footprints, and power consumption by 60-80% with minimal loss in model intelligence, facilitating edge-device deployments.
Why choose our AI Model Compression (Tensor Networks)?
We offer proprietary integration layers and guaranteed performance benchmarks, ensuring a seamless and future-proof implementation.
What We Offer
- Tensor Train & MPS Decomposition
- Low-Rank Model Reconstruction
- Parameter & VRAM Reduction
- Edge-AI Porting & Optimization
- Quantization & ONNX Compile Pipelines
Technical Stack
Implementation Workflow
Base Model Evaluation
Measure the weight density, memory footprint, and bottleneck layers of the target model.
Tensor Decomposition
Apply Matrix Product States or Tensor Train algorithms to decompose heavy weight layers.
Fine-Tuning & Distillation
Conduct post-decomposition fine-tuning to recover minor accuracy drops.
Optimization & Packaging
Export to optimized runtimes (ONNX/TensorRT) with custom C++ operators.
Edge Deployment
Compile for edge hardware and verify execution speed, VRAM limits, and energy usage.
Explore Other Services
Post-Quantum Cryptography (PQC)
Next-generation cryptographic defense scanning and migration pathways to protect enterprise systems from future quantum decryptions.
Quantum-Inspired Optimization
GPU-accelerated optimization pipelines designed to solve high-complexity logistics, portfolio, and supply-chain bottlenecks today.
Quantum Machine Learning (QML)
Building hybrid quantum-classical neural networks and kernel models designed to process complex high-dimensional datasets.
Quantum Advisory & Feasibility
Deep tech feasibility reviews, technical research papers, and rapid MVP design to future-proof enterprise strategies.