🚀 Introducing MemCloud — Pool Unused RAM Across Machines on Your LAN (Rust, Zero-Config)

Published: (December 7, 2025 at 03:05 PM EST)
2 min read
Source: Dev.to

Source: Dev.to

Introduction

Hey DEV community! 👋
I’ve been working on a side project that turned into something surprisingly useful, fun, and very Rust‑y.

MemCloud is a distributed in‑memory data store that lets multiple machines on your LAN pool their RAM into a single ephemeral “memory cloud.”

Use case: Have a Mac, a Linux machine, and a spare mini‑PC sitting around? MemCloud turns them into one big RAM cache — automatically.

I often run ML experiments, dev servers, and log processors that overflow RAM on one machine while another machine sits idle right next to it. I wanted a tool that:

  • works offline
  • runs locally
  • requires zero configuration
  • discovers peers automatically
  • lets me store/load data across devices in milliseconds

So I built MemCloud, a tiny Rust daemon + CLI + SDKs that create a peer‑to‑peer RAM mesh on your LAN. Every memnode contributes its RAM to the cluster. A write on Machine A can be read from Machine B in under 10 ms. Just start the daemon — peers auto‑discover each other. No IPs, no ports, no YAML files, no Kubernetes. No cloud. No accounts. No central server.

  • memcli for terminal workflows
  • Rust SDK for systems work
  • TypeScript SDK for JS/Node devs

Storage Modes

Block Store

Raw bytes & streams.

Key‑Value Store

Redis‑style set / get.

Architecture

You can view the architecture diagrams here:
➡️

  • Each node runs a small daemon (memnode).
  • SDKs and CLI talk only to the local daemon.
  • The daemon handles routing and storage across peers.

Quick Install (macOS & Linux)

# Install via script
curl -fsSL https://raw.githubusercontent.com/vibhanshu2001/memcloud/main/install.sh | sh
# Build from source
git clone https://github.com/vibhanshu2001/memcloud.git
cd memcloud
cargo build --release
  • GitHub:
  • Docs:
  • NPM package:

Feedback

I’d love feedback on:

  • performance ideas
  • networking improvements
  • memory/eviction strategies
  • real‑world use cases

Thanks for reading!

— Vibhanshu

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