How To Combine Laptops To Run Large Llms
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What “combine laptops” actually means
A single laptop with 16 GB of RAM can run a 7B model and feel snappy. It cannot run Llama 3.3 70B or Qwen 3.5 72B. The fix isn’t a $5,000 GPU upgrade — it’s pooling the machines you already own. With the right runtime, three or four laptops can cooperatively load a model that none of them could hold alone, and serve it at the speed of the slowest one in the ring.
The four runtimes worth knowing in 2026
You don’t need identical machines. A 64-GB Mac Studio, a 32-GB ThinkPad, and a 16-GB MacBook Air can all join the same pod — the bigger machines just carry more layers. Your bottleneck becomes the slowest member, not the smallest.
1. Hyperspace pods (easiest, OpenAI-compatible)
exo (from exo Labs) is an open-source distributed inference engine that auto-discovers machines on your local network and shards models across them by available memory. It runs on macOS, Linux, iPhone, iPad, and Android, and it’s especially fast on unified-memory Apple Silicon because there’s no copy across PCIe. Single command to start a node:
2. exo (terminal-first, Apple Silicon shines)
Petals is a BitTorrent-style network for LLMs: anyone can contribute spare compute, anyone can join and run inference against a model that’s currently loaded across the swarm. It’s the right choice if you want to run a 405B model and you’re OK with multi-second per-token latency from public-network hops. Not the right choice for low-latency local pods on the same LAN.
3. llama.cpp RPC (most control, lowest dependencies)
Total available memory across the cluster has to exceed the model’s on-disk size at your chosen quantization, plus context-window overhead. Rough rules:
4. Petals (truly distributed across the internet)
Pipeline parallelism shuttles activations between layers across the network on every token. The tensor sizes are small (typically 4–16 KB per token at 8B–70B scales), so latency hurts more than bandwidth. In rough order of best-to-worst: