USE CASE / 02

Compute is everywhere.
Connect it.

Connect GPUs across clouds and homes for distributed training and inference.

“Doubling the network speed halves our compute budget. That’s the difference between a $1M model and a $500K model.”

Ari LotterNous Research
[01]

Coordinate the swarm

Use gossip to coordinate peers across providers.

[02]

Move the heavy parts

Transfer model data with blobs. Your app handles scheduling.

[03]

Keep another path open

Relay fallback keeps otherwise unreachable peers connected.

IN PRACTICE

Built with iroh.

Use case · Distributed training

Nous Research / Psyche

Psyche brings GPUs across clouds and homes together to train models. Nous Research uses iroh gossip to coordinate the peers and blobs to move training data.

Explore Nous Research↗
Psyche’s distributed training dashboard, showing its compute pool, contributor globe, and model training runs

Use case · Shared inference

Block / Buzz

Buzz brings people and agents into one workspace. MeshLLM is built into Buzz, letting community members opt in to share spare compute and run open models together over iroh.

Read the Buzz story↗
People and agents collaborating in a Buzz engineering channel

“With iroh, every node is just a public key. We pool GPUs across clouds and closets into one mesh. No central server, no NAT config.”

Block
Michael NealePrincipal Engineer at Block

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