One platform, the full range. The same Eldric installs on an edge box and on a multi-GPU datacenter cluster — you add nodes, not new products. Here's what that buys you at the top of the range.
The platform that runs on a Raspberry Pi is the platform that runs on a rack of GPUs — only the activated modules differ. Start small, grow by adding nodes; nothing to re-architect when you scale up.
On multi-GPU machines Eldric places models for you: a model per card, a large model split across cards, or many small models packed together. Capacity grows with the hardware you add.
Run training rounds across nodes and sites where each site keeps its own data: the platform coordinates the round and aggregates the result, so a multi-site organisation can improve a shared model without pooling raw data centrally.
Run multiple controllers so the cluster keeps serving if one fails, and federate clusters across sites into one platform — hierarchical or peer mesh. Clustering & HA →
For research and HPC workloads, the platform brings 140+ scientific data APIs and the agent / retrieval / training stack — all on infrastructure you control. For research →
Hardware-agnostic — CPUs, NVIDIA, AMD, Apple Silicon, ARM. As a datacenter OS · Get started.