Purpose-built AI chips with dedicated throughput — for Fluents deployments that can't share inference capacity.
SambaNova builds custom AI hardware — Reconfigurable Dataflow Units (RDUs) — designed for high-throughput, consistent-latency LLM inference at enterprise scale. Unlike cloud GPU inference which shares capacity across many users, SambaNova's enterprise deployments provide dedicated compute — meaning consistent performance regardless of what other customers are doing on shared infrastructure.
For Fluents deployments running very high simultaneous call volumes where latency spikes are unacceptable, SambaNova provides the hardware guarantee that shared cloud inference can't.
Dedicated RDU hardware means Fluents' conversation engine inference never competes with other customers' workloads — consistent latency at peak call volumes
High-throughput architecture designed for sustained enterprise workloads — not bursting, not rate-limited, not variable
Available for large enterprise Fluents deployments running tens of thousands of simultaneous calls with strict SLA requirements
The Shared Infrastructure Problem at Scale
Cloud GPU inference platforms — including those used by most LLM providers — share capacity across customers. At peak hours, latency increases. During provider incidents, queues back up. For an insurance carrier processing 5,000 simultaneous FNOL calls after a major weather event, or a healthcare network running same-day reminder calls at 8am, that variability is unacceptable. SambaNova's dedicated hardware eliminates it.
Large Insurance Operations: Guaranteed Performance During Peaks
After a hurricane makes landfall, an insurance carrier's call volume spikes dramatically. Outbound FNOL intake calls need to go out immediately and complete quickly. With dedicated SambaNova inference, the conversation engine capacity is reserved — Fluents processes calls at full speed regardless of what's happening on shared cloud infrastructure.
Healthcare Networks: Morning Rush at Scale
A large hospital network sending appointment reminders to 50,000 patients at 7am needs all those calls to complete within a narrow window. Dedicated inference capacity means the 50,000th call processes with the same speed as the first — not queued behind shared-infrastructure load.