Agentic AI
Agentic AI is not a single inference call. Every reasoning step, retrieval, tool call, and validation can trigger more model calls, straining orchestration, memory, and fabric. The OPU executes multi-step agents with lower latency, less data movement, and higher utilization.
Built for agent traffic
The OPU addresses the bottlenecks agentic workloads create: inference fan-out, real-time orchestration, and memory and fabric pressure that conventional GPU stacks were not designed for.
Higher utilization, lower cost per task
Keep accelerators busy across multi-agent workflows, driving down the cost of every reasoning step, tool call, and retrieval.
Less data movement
Unified memory and the MultiPlexus fabric keep agent state close to compute, cutting independent CPU, GPU, and NIC handoffs.
Real-time orchestration
TAOS schedules graphs of model calls natively, so routing, validation, and fan-out execute with deterministic low latency.
The economics of agentic inference
6000
Token/Second/User
90 %
Lower cost per token
Built for inference fan-out
In agentic systems, each decision can trigger additional model calls across planners, tools, retrievers, and validators. The OPU was architected for exactly this pattern, pairing compute, unified memory, and a scale-up fabric so fan-out scales without the cost and latency of fragmented infrastructure.
One architecture, no handoffs
The OPU integrates AI OmniCores, Arm cores, and a unified memory hierarchy in a single device, so agent state, caches, and intermediate results stay in place instead of bouncing between CPUs, GPUs, and NICs. Less data movement means lower latency per step, more predictable tail behavior, and more of every watt spent on useful inference rather than moving bytes.
A fabric that scales with your agents
MultiPlexus links OPUs across systems with load/store semantics and low-hop latency, forming one coherent compute domain. TAOS schedules multi-agent graphs across it natively, routing reasoning steps, tool calls, and retrievals to available compute. Utilization stays high as workloads grow, and the cost per agent task keeps falling as you scale from one system to a full rack.
Related insights
Our architecture provides unmatched computational efficiency, allowing intricate AI models to operate with low latency and high throughput.
Beyond GPUs: why we built Tsavorite
Our view is that AI has outgrown the way the industry builds computers for it. Adding more of the same hardware is no longer the answer. AI needs an architecture designed for it from the start, one that becomes more efficient as it grows rather than less.
Tsavorite Scalable Intelligence Sets New Frontier for AI Efficiency and Scale with the Omni Processing Unit
Run agents at infrastructure scale
See how the OPU changes the economics of agentic inference. Talk to us about evaluation systems and deployment.
Talk to our team
