SambaNova builds a full-stack AI inference platform centered on custom dataflow chips (RDUs) and a three-tier memory architecture designed to address latency and energy efficiency bottlenecks in generative AI deployment. The architecture targets enterprise and government workloads requiring on-premises or sovereign deployment - fine-tuning open-source models behind customer firewalls with full data and model ownership retention. The platform powers sovereign AI data centers across Australia, Europe, and the UK, focusing on avoiding vendor lock-in to proprietary inference services.
The technical approach uses custom dataflow technology rather than GPU-based architectures, trading off ecosystem maturity for claimed improvements in inference throughput and energy consumption at scale. The three-tier memory design addresses memory bandwidth constraints common in transformer inference. The platform supports PyTorch-based model fine-tuning and deployment workflows, with integration points through Python and C++ APIs. Operational complexity centers on full-stack ownership - hardware, software, and deployment infrastructure - requiring coordination across chip design, systems software, and model serving layers.
The stack includes standard ML tooling (PyTorch, Python) alongside proprietary components for the RDU runtime and memory management. Build and CI infrastructure uses Bazel and CircleCI; artifact management through Google Artifact Registry and JFrog. The deployment model targets enterprises prioritizing data sovereignty over cloud-based inference APIs, introducing trade-offs in operational overhead versus control and latency predictability for on-premises workloads.