Honest limits — read before you rely on DaisyChain

DaisyChain is a real tool for a specific job: chaining spare/old machines to train small models faster by pooling their compute. It is not magic, and it is easy to misapply. Here's the honest envelope.

What it does

Data-parallel training: every node holds a full copy of the model and trains on its slice of the data; gradients are averaged across the network.
Capacity-weighted: each node measures its own speed (CPU or GPU) and takes a proportional batch, so a faster machine does more. GPU nodes auto-join and carry more load; CPU-only nodes still participate.

What it does NOT do (expect these)

❌ Not GPU-class training. N pooled old CPUs/GPUs are still that class of hardware. A single modern GPU will beat the whole cluster for real models.
❌ Pools compute, not memory. Every node needs the whole model in RAM (or VRAM). You cannot train a model bigger than a single node can hold. Chaining 5 laptops does not give you one big machine.
❌ Sublinear scaling. Gradient sync over WiFi is bandwidth-bound, and the slowest node paces every step (synchronous). Each added node helps less than the last; a slow link can erase the benefit.
❌ Not for huge/modern models. Transformers/large CNNs at CPU (or old-GPU) speed are impractically slow.

The sweet spot

Small models (small MLPs, tabular/classifier tasks, small fine-tunes), a handful of machines, model fits on each, you want more throughput and don't have a GPU handy. Education, research, retro/old-hardware hobby clusters.

Hardware / OS

Multi-node reliably runs on Linux/macOS. On Windows, gloo tensor collectives are unstable — use Docker or WSL. Single-machine use is fine on Windows.
Old GPUs must be new enough for current PyTorch (compute capability ≳ 5.0, roughly GTX 900 / Maxwell and up). Kepler/Fermi-era cards won't work with modern torch.
Put all nodes on the same interface (Tailscale tailscale0, or a real LAN NIC) via GLOO_SOCKET_IFNAME — not a VPN/Docker/WSL virtual NIC.

The one-line version

You can chain old machines to train a small model together, faster by throughput, with honest sublinear scaling — and DaisyChain won't pretend to be more than that.