Multiverse Computing partnered with a national defense R&D organization to bring advanced multimodal AI directly onboard robotic platforms operating in defense-grade environments. By compressing a 72-billion-parameter Vision-Language Model into a footprint that fits on edge robotic systems, the project enables real-time vision-language reasoning on the platform itself, without sending data off-device and without compromising the operational accuracy required for mission-critical use cases.
The Challenge
The client needed to deploy a 72-billion-parameter multimodal Vision-Language Model on resource-constrained robotic platforms for real-time reasoning. The deployment had to meet strict requirements in memory, compute, energy efficiency and data privacy, and preserve the model's multimodal accuracy for mission-critical use cases. Standard, uncompressed models of this scale cannot run on the embedded hardware found on tactical robots, and offloading to the cloud was not viable for confidentiality and connectivity reasons.
Our Solution
Multiverse Computing delivered a compressed, quantum-inspired optimized version of a state-of-the-art 72B Vision-Language Model, tailored for real-time deployment on edge robotic systems. The compressed Multiverse Computing model reduces memory and compute requirements while preserving multimodal reasoning accuracy, and is delivered ready for the client to fine-tune securely with private defense data.
- Quantum-inspired compression of a 72B multimodal Vision-Language Model with preserved multimodal reasoning accuracy.
- Hardware-aware optimization for edge robotic platforms, enabling real-time vision-language inference under tight resource constraints.
- Delivery of a general compressed model ready for secure fine-tuning with private defense data.
- Architecture reusable across different robotic and autonomous hardware systems.
Results
On-edge multimodal reasoning directly on the robotic platform
Privacy-sensitive, defense-grade deployment with no cloud dependency
A 72B-parameter Vision-Language Model running under tight hardware constraints
Reduced memory, compute and energy footprint per inference
General compressed model ready for secure fine-tuning with private data
Reusable architecture across different robotic and autonomous systems
Strategic outcome: a defense-grade multimodal AI capability that brings vision-language reasoning directly onto the robotic platform, preserves model accuracy, and gives the client full control over data, fine-tuning and deployment.
