Running AI Memory on a Raspberry Pi

Edge AI is only useful if it actually runs on edge devices. Here's how to deploy shodh-memory on a Raspberry Pi 4/5 and achieve sub-100ms semantic search.

Why Raspberry Pi?

The Pi represents the baseline for edge computing:

If your AI memory system can't run here, it's not really edge-ready.

Installation

On Raspberry Pi OS (64-bit recommended)

curl -L https://github.com/varun29ankuS/shodh-memory/releases/download/v0.1.80/shodh-memory-aarch64-linux -o shodh-memory

chmod +x shodh-memory

./shodh-memory --data-dir ./memory-data

That's it. Single binary, no Python, no npm, no Docker.

Memory Configuration

For a Pi 4 with 4GB RAM, we recommend:

config.toml

[memory]

max_memories = 50000

embedding_cache_size = 1000

graph_cache_size = 10000

[performance]

worker_threads = 4

batch_size = 32

Benchmark Results

On a Raspberry Pi 4 (4GB, arm64):

These numbers assume warm cache. Cold start adds ~200ms for model loading.

Integration with Robotics

For ROS2 integration:

from shodh_memory import Memory

import rclpy

class MemoryNode(Node):

def __init__(self):

super().__init__('memory_node')

self.memory = Memory('./robot_memory')

self.create_subscription(

String, 'observations', self.observe, 10)

def observe(self, msg):

self.memory.remember(msg.data, tags=['observation'])

Power Consumption

A 10,000mAh battery provides ~6 hours of active use.

Conclusion

Edge AI memory isn't a future promise—it's available now. The Pi proves that meaningful AI can run on meaningful hardware constraints.