Why Pooling Local RAM Beats Buying Bigger Machines
We've all been there.
You’re running a heavy build, training a model, or processing a massive dataset. Suddenly, everything grinds to a halt. You check htop and see the red bar of death: Swap. Your 32GB MacBook is gasping for air.
Meanwhile, your coworker’s laptop is sitting idle on the desk next to you. The office server is humming along at 5% utilization.
In that moment, the typical engineer’s instinct (including mine) is: "I need a bigger machine."
We instinctively reach for the credit card to upgrade to 64GB or 128GB. But lately, I’ve realized that this instinct isn’t just expensive—it’s technically backwards.
The "Bigger is Better" Trap
The assumption is simple: More RAM on one machine = better performance.
It feels true because local memory is usually the fastest thing we have. But there’s a catch that I learned the hard way while building distributed systems.
As you scale up a single machine, you hit a wall.
When you buy a massive workstation or a high-memory cloud instance, you aren't just getting more RAM; you're getting more headaches:
- Bandwidth bottlenecks: A single memory bus can only push so much data.
- NUMA issues: On big multi-socket servers, accessing RAM on the "other" CPU plays havoc with latency.
- The Blast Radius: If that one expensive machine crashes, your entire workload dies with it.
Compare that to the laptop or server sitting next to you. It has its own memory controller, its own bus, and its own CPU.
Memory bandwidth scales linearly when you go wide. Two machines with 64GB RAM have roughly double the aggregate bandwidth of one machine with 128GB.
Why We Don't Share
So if "going wide" is better, why don't we do it for memory?
Because it's hard.
We have great tools for sharing CPU (Kubernetes) and storage (S3, network drives). But memory? Memory has always been trapped inside the box. It’s strictly "local."
This leads to what I call Stranded RAM.
Right now, if you look around your office or data center, about 60-80% of the total RAM is doing absolutely nothing. It's provisioned, paid for, and powered on—but it's completely inaccessible to the one process that actually needs it.
It's like having five cars in your driveway but being unable to drive to work because the one you're sitting in is out of gas.
Enter MemCloud
I built MemCloud because I wanted to break this limitation. I wanted to treat the RAM across my local network—my laptop, my desktop, my Raspberry Pi cluster—as one single, giant pool of memory.
MemCloud doesn't replace your local RAM. That would be silly; network latency is real.
Instead, it fits into the "warm" layer of the hierarchy:
- CPU Cache (Instant)
- Local RAM (~100 nanoseconds)
- MemCloud / Remote RAM (~10-30 microseconds)
- NVMe SSD (~100 microseconds)
- Disk (Milliseconds)
Remote RAM is still 5-10x faster than an NVMe SSD.
For things like build caches, ML embeddings, temporary compiler artifacts, or analytics scratch space, it is the perfect middle ground. You get the speed of memory without the cost of a monster workstation.
Real Numbers
To prove to myself this wasn't just a fun theory, I benchmarked it.
| Storage Type | Latency | What it feels like |
|---|---|---|
| Local RAM | ~0.1 µs | Instant |
| Pooled RAM (LAN) | ~10–30 µs | Extremely Snappy |
| NVMe SSD | ~100 µs | Fast I/O |
| Cloud Object Store | ~50,000 µs | Waiting for a download |
When you offload a few gigabytes of "warm" data to a neighbor node, your local machine breathes a sigh of relief. The swap thrashing stops. The UI becomes responsive again.
The Vision: Infrastructure as a Commons
There is a cost argument here—using what you already have is cheaper than buying new gear. But for me, the exciting part is the shift in mindset.
When we view memory as a shared resource rather than a private possession of a single kernel, amazing architectures become possible.
- Your CI pipeline can borrow 100GB of RAM from office workstations at night.
- Edge devices can pool resources to run AI models they couldn't handle individually.
- Teams can share a massive in-memory dataset without everyone needing a copy.
I’m building MemCloud in Rust because I believe this is where systems are heading. We're moving away from monolithic giants toward collaborative, peer-to-peer swarms.
If you've ever stared at a "Out of Memory" crash while surrounded by idle computers, you know why this matters.
Give MemCloud a spin, check out the docs, or browse the code on GitHub. I'd love to hear if this solves a real headache for you.