In this Edge AIsle episode, we summarize the key insights from Hailo edge AI’s blog Bringing Generative AI to the Edge: LLM on Hailo-10H. As generative AI continues to reshape industries, Large Language Models are becoming increasingly powerful, but also increasingly demanding in terms of compute, memory, and cost. This episode explains why traditional cloud-based LLM deployment introduces latency and expense, and why edge-based inference is emerging as a critical alternative. 

We focus on how the Hailo-10H LLM accelerator enables efficient LLM inference directly on the device, addressing core edge constraints such as limited memory and power consumption. The discussion highlights why real-world LLM performance is measured using metrics like Time-to-First-Token (TTFT) and Tokens-Per-Second (TPS), and how near-instantaneous responses become possible when models are run locally rather than in distant data centers. 

Finally, the episode explains the practical benefits of edge-deployed LLMs: improved privacy, lower operational costs, and greater efficiency. By keeping data on the device, sensitive information never leaves the system, and reliance on continuous cloud compute is reduced. Techniques such as LoRA (Low-Rank Adaptation) make it possible to run powerful generative models at the edge without sacrificing performance. Discover how Hailo is enabling practical generative AI everywhere at https://hailo.ai/technology.

Chapters:
(00:00) Introduction: Generative AI and LLMs at the Edge

(00:21) The Challenge of Large Language Models

(00:35) Introducing the Hailo-10H AI Accelerator

(00:55) Performance Metrics: TTFT and TPS

(01:11) Privacy Through Local LLM Inference

(01:15) Cost Reduction by Avoiding the Cloud

(01:28) LoRA and Efficient Edge LLM Deployment

(01:40) The Future of Generative AI at the Edge

(01:46) Closing Thoughts from Hailo

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