Local LLMs on Raspberry Pi: What the AI HAT+ 2 Can and Cannot Do
The Raspberry Pi AI HAT+ 2 is Raspberry Pi’s first serious attempt at covering the generative AI gap on the Pi 5. It pairs a Hailo-10H accelerator with 8GB of onboard LPDDR4X and targets small, fully local LLM and VLM workloads without touching the Pi’s system RAM.
For local LLM enthusiasts, this is not a GPU replacement and it is not trying to be. It is a low power, fixed function accelerator aimed at int4 quantized models in the 1B to roughly 1.5B parameter range.
Hardware and Architecture
At the core is the Hailo-10H, rated at 40 TOPS int4 and 20 TOPS int8. The important change versus the original AI HAT+ is the dedicated 8GB of memory attached directly to the accelerator. Models live entirely on the HAT, not in Pi RAM, which avoids starving the CPU and keeps the PCIe link mostly idle once the model is loaded.
Power draw is modest, around a few watts under sustained load, and cooling is handled with a small heatsink. The tradeoff is that the HAT occupies the single PCIe lane on the Pi 5, so you are choosing between this and NVMe storage.
Memory bandwidth is roughly in the same class as the Pi 5’s LPDDR4X. That matters because for autoregressive LLMs, bandwidth limits token throughput long before raw TOPS do.
What Models Actually Make Sense
At launch, the supported models include Llama 3.2 1B, Qwen 2 and 2.5 variants at 1.5B, and DeepSeek R1 Distill at 1.5B. These are int4 optimized builds intended specifically for the Hailo runtime.
👁 raspberry pi hat plus 2 running running qwen21.5b llm
In testing, token rates land in the low single digits to around 8 tokens per second depending on model size and structure. In several cases, the Pi 5 CPU can actually run the same small models faster if you let it consume all cores and system memory. That is not a flaw in the HAT so much as a reflection of memory bandwidth limits and conservative power budgets on the accelerator.
Where the HAT makes sense is not peak speed, but isolation. Running the model on the HAT leaves CPU cores and RAM free for everything else.
Practical Use Cases
For general chat or coding assistants, these models are clearly limited. Reasoning depth and general capability are nowhere near what even a heavily quantized 7B model can do on a used GPU. Sorting lists, writing nontrivial code, or open ended reasoning will show the cracks quickly.
Where the AI HAT+ 2 fits is edge style workloads. Always on wake word detection, simple command parsing, short translations, structured classification, embeddings, and tightly scoped assistants are realistic. Vision plus language pipelines also make sense, since the Hailo stack is already strong in real time computer vision and can run both classes of models without loading the Pi CPU.
👁 raspberry pi hat plus 2 running running vision model
Battery powered robots, kiosks, and offline assistants are the clearest matches. In those cases, a few watts for local inference with predictable latency is more valuable than raw tokens per second.
Performance per Dollar Perspective
At $130, this is not competing with used GPUs for local LLM work. A second hand 12GB or 16GB GPU will run circles around it for anything above tiny models. Even a 16GB Pi 5 running llama.cpp can handle much larger compressed models with better output quality, albeit slowly and while consuming the whole system.
The value here is not scale, but specialization. You are paying for dedicated memory, low power inference, and clean offload from the main system. If your goal is running 7B, 13B, or larger models, this is the wrong tool. If your goal is small, always on, private AI at the edge, the AI HAT+ 2 finally makes that practical on Raspberry Pi hardware.
For local LLM enthusiasts, it is best viewed as a niche accelerator rather than a general purpose inference device.
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