Deploying LLMs at the edge is hard due to size and resource limits. This guide explores how progressive model pruning enables scalable hybrid cloud–fog inference.

Large Language Models (LLMs) have become backbone for conversational AI, code generation, summarization, and many more scenarios. However, their deployment poses significant challenges in environments where compute resources are limited mostly in hybrid cloud-fog architectures, where real-time inference may need to run closer to the edge.

In these instances, progressive model pruning plays a pivotal role offering solution to reduce model size and computation cost without impacting accuracy. In this article, we will discuss how to efficiently deploy LLMs across cloud-fog topologies using layer-aware, resource-adaptive pruning techniques.

What Is a Hybrid Cloud-Fog Topology?

Before we deep into the topic, let’s understand and define the architecture:

• Cloud layer: This layer consists of centralized data centers that contains thousands of High-Performance Computing servers (HPC – GPU/TPU) with large capacity for training large language models (LLM), full-scale inference, and orchestration.
• Fog layer: This layer is different from traditional cloud layer where we ...