SambaNova

Executes large language model inference using custom SN50 Reconfigurable Dataflow Unit (RDU) chips with dataflow-based architecture optimized for token generation. Routes requests through SambaNova's proprietary inference stack that bundles multiple frontier-scale models (Llama and open-source variants) on single nodes, leveraging three-tier memory hierarchy for reduced latency and improved throughput compared to traditional GPU tensor cores. Supports heterogeneous inference patterns via Intel partnership (GPUs for prefill phase, RDUs for decode phase, Xeon CPUs for tool execution).

Enables deployment of multiple frontier-scale language models on a single SambaNova node through infrastructure-level model bundling, managed via SambaStack orchestration layer. Abstracts model selection and routing logic, allowing dynamic switching between models based on inference requirements without requiring separate hardware provisioning per model. Supports heterogeneous compute allocation where prefill, decode, and tool-execution phases route to optimized hardware (GPUs, RDUs, CPUs) within single deployment.

Provides enterprise deployment infrastructure with data residency guarantees across sovereign AI data center partners in Australia, Europe, and United Kingdom. Enables organizations to run inference workloads in geographically-isolated environments meeting regulatory requirements (GDPR, data sovereignty laws) without data transiting through US-based infrastructure. Deployment model and compliance certifications not documented in available materials.

Optimizes inference pipeline specifically for agentic AI workloads combining language generation with tool-calling and function execution. Leverages heterogeneous compute architecture where RDU chips handle token generation (decode phase), GPUs accelerate prefill phase for context processing, and Xeon CPUs execute tool invocations. Bundles multiple models on single node to support dynamic model selection based on task complexity (fast models for simple tool-calling, larger models for reasoning).

Executes LLM inference using proprietary SN50 RDU (Reconfigurable Dataflow Unit) chips with dataflow-based compute architecture instead of traditional GPU tensor cores. Eliminates GPU dependency for inference workloads, reducing power consumption and cost per token through purpose-built silicon optimized for agentic inference patterns. Three-tier memory hierarchy (claimed but unspecified) reduces memory bandwidth bottlenecks compared to GPU memory hierarchies.

Provides enterprise-grade deployment options (on-premise, managed cloud, or hybrid) with infrastructure flexibility to bundle multiple models on single nodes and customize hardware allocation. Supports heterogeneous compute configurations combining RDU chips, GPUs, and CPUs for different inference phases. Deployment model, scaling mechanisms, and multi-node orchestration details not documented in available materials.

Provides end-to-end AI platform combining custom silicon (RDU chips), inference optimization (SambaStack), and enterprise deployment infrastructure as integrated system. Eliminates fragmentation of separate model providers, inference engines, and deployment platforms by optimizing entire stack (hardware, software, infrastructure) for agentic AI workloads. Integration points and optimization mechanisms not detailed in available documentation.

Claims 3X cost savings for agentic AI inference workloads compared to competitive inference platforms, attributed to RDU custom silicon efficiency and heterogeneous compute architecture. Savings mechanism is based on 'tokens per watt' efficiency and decode-phase optimization, but baseline comparison, pricing structure, and cost calculation methodology are not documented.