I’m a developer deeply invested in pushing the boundaries of edge AI, particularly for Large Language Model (LLM) RAG applications.
With the exciting specifications of the upcoming Hailo-10H and its focus on generative AI, I see significant potential for it to compete directly with, or even surpass, solutions like the NVIDIA Jetson Orin Nano Super Developer Kit for specific LLM use cases.
My current consideration is to build a high-performance, power-efficient local LLM inference platform by utilising two Hailo-10H modules on a dual M.2 board with the Raspberry Pi 5.
My primary goal is to create a robust, real-time local coding assistant, similar to GitHub Copilot, which demands both speed and low latency.
To assess the viability of this advanced setup as a compelling alternative to GPU-based solutions, I have some key questions:
Multi-Device Software and LLM Orchestration:
What is Hailo’s current and planned software support (SDK, APIs, tools) for running a single LLM inference across multiple Hailo-10H accelerators?
For instance, can we leverage model parallelism, pipeline parallelism, or advanced batching effectively over two chips?
Do you plan to provide a high-level framework or simplified APIs that would allow developers to seamlessly orchestrate and load balance a single LLM across two Hailo-10H units, much like how NVIDIA optimises for multi-GPU setups?
Are there any internal benchmarks or examples for multi-Hailo-10H LLM inference that you can share, or that are planned for release?
LLM Optimization and Compatibility:
Given the Hailo-10H’s 40 TOPS (INT4), what are the recommended quantisation levels for achieving optimal LLM performance on your hardware, particularly for code generation models (e.g., 7B parameter Code Llama)?
How does your compiler maximize efficiency at these precisions?
How does Hailo’s compiler and runtime specifically optimise the unique components of LLMs, such as the attention mechanism and KV cache management, on the Hailo-10H architecture?
Will Hailo provide pre-optimised versions of popular LLMs in the HEF format to simplify deployment?
Raspberry Pi 5 Integration and Performance Considerations:
The Raspberry Pi 5’s PCIe interface (Gen 2.0 x1, or forced Gen 3.0) is a known factor.
How would the system manage data transfer and communication with two Hailo-10H modules simultaneously over this limited bandwidth?
What are the potential real-world bandwidth limitations we might encounter, and how could they impact the performance of a dual-Hailo-10H setup for LLMs on the Pi 5, especially with larger models or extended context lengths?
Do you have any recommendations for dual M.2 HATs or boards that would be optimal for integrating two Hailo-10H modules with the Raspberry Pi 5, considering power delivery and PCIe switch performance?
Competitive Positioning and Use Case Viability
From Hailo’s perspective, how does a dual Hailo-10H setup on a Raspberry Pi 5 stack up against the NVIDIA Jetson Orin Nano Super for complex, real-time LLM applications?
Where do you see its key advantages?
Are there any specific challenges or limitations you foresee when attempting such a high-performance, multi-accelerator LLM setup that would be critical for me to understand in advance?
I believe a successful dual-Hailo-10H solution could truly democratise powerful local LLM inference, offering a compelling, power-efficient, and cost-effective alternative to GPU-centric edge platforms.
Thank you for your time and detailed insights into Hailo’s cutting-edge technology.