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Raspberry Pi Adds 8GB RAM for Local Gen AI

Raspberry Pi AI Generative AI AI HAT Llama 3.2 DeepSeek-R1-Distill Tech Gadgets AI Models
January 15, 2026
Viqus Verdict Logo Viqus Verdict Logo 7
Edge AI Experimentation
Media Hype 6/10
Real Impact 7/10

Article Summary

Raspberry Pi is expanding its presence in the burgeoning generative AI space with the release of the AI HAT+2, a new add-on board designed for the Raspberry Pi 5. Priced at $130, this module offers 8GB of RAM and incorporates a Hailo 10H chip boasting 40 TOPS of AI performance. The HAT+2 shifts AI-related workloads to this dedicated component, freeing up the Raspberry Pi 5’s Arm CPU for other tasks. Unlike its predecessor, which focused on image-based AI, the HAT+2 is capable of running small gen AI models like Llama 3.2 and DeepSeek-R1-Distill, alongside Qwen models, allowing for training and fine-tuning. A demonstration showcased its ability to generate text descriptions from camera streams and translate French to English. However, a comparative test by tech YouTuber Jeff Geerling revealed that a standalone Raspberry Pi 5 with 8GB of RAM often outperformed the HAT+2, attributed to power draw limitations of the HAT+2 (3W vs. Pi 5’s 10W). Raspberry Pi indicates further larger models are in development for future updates.

Key Points

  • Raspberry Pi released the AI HAT+2, an $130 add-on board for the Raspberry Pi 5.
  • The HAT+2 includes 8GB of RAM and a Hailo 10H chip for AI processing.
  • The board allows for local execution of small generative AI models like Llama 3.2 and DeepSeek-R1-Distill.

Why It Matters

This news is significant because it democratizes access to generative AI. Previously, running these models required substantial computing power and expensive hardware. The Raspberry Pi’s offering brings AI capabilities to a broader audience—developers, hobbyists, and educational institutions—potentially fostering innovation and experimentation. Furthermore, it highlights the growing trend of edge computing, bringing AI processing closer to the data source, reducing latency and bandwidth requirements. This has major implications for IoT and real-time applications.

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