Hybrid Large Language Models To Improve On-premise Deployments with Concrete ML
Blog post from Zama
The text explores the trade-offs between cloud and on-premise deployment of large language models (LLMs), highlighting the challenges of data privacy, model IP protection, and compliance with licensing agreements. It introduces a hybrid approach using Concrete ML's Fully Homomorphic Encryption (FHE), which allows sensitive data processing to be offloaded to the cloud while preserving user privacy and maintaining the on-premise performance. The hybrid model leverages the HybridFHEModel class to convert PyTorch models into hybrid ones, enabling some layers to run on encrypted data remotely. This approach aims to balance user privacy, model security, and operational efficiency, illustrated by benchmarking the phi-1.5 model, showing a generation latency of 2.5 seconds per token compared to 50ms for fully on-premise setups. Future improvements in Concrete ML aim to reduce latency and data transfer sizes through innovations like ciphertext seeding and GPU utilization for server-side computation.
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