Global Inference at the
Speed of Thought
Eliminate redundant compute operations and maximize your KV Cache. Deliver seamless, low-latency conversational AI globally through a unified engine featuring Context-Aware Prompt Routing, Intelligent Pod Selection, Model-Centric Autoscaling, and Dynamic LoRA Adapter Routing.
Context-Aware Prompt Routing
Traditional load balancers distribute inference requests without context, forcing silicon to repeatedly re-process identical prompt prefixes. This creates severe pre-fill latency for conversational AI workflows.
Tensor Axiom introduces a structural shift. Powered by a content-addressing chained hash scheme, the engine tracks the exact physical location of active cache states across the planetary fleet in near-real-time, steering payloads directly to the optimal node.
Intelligent Model & Pod Selection
Hardware availability is only half the equation. Our orchestration layer is designed for complete model-awareness. By integrating advanced container selection heuristics, the platform dynamically provisions and routes payloads to pods based on specific model availability and hardware state requirements.
Whether scaling massive endpoints or lightweight quantized deployments, the engine automatically evaluates queue depths and active KV cache states to steer the workload to the optimal pod, maximizing throughput without manual intervention.
Model-Centric Autoscaling
Tensor Axiom independently scales discrete inference engines based on hardware-specific telemetry, such as queue depth and GPU cache pressure. By normalizing divergent metrics from engines like vLLM and SGLang, the control plane dynamically manages model-specific pods without relying on generic CPU or memory thresholds.
This model-aware approach ensures that resource-intensive topologies scale independently from smaller auxiliary models, guaranteeing strict multi-tenant SLAs while minimizing cloud infrastructure waste.
Dynamic LoRA Adapter Routing
Tensor Axiom features intelligent, state-aware routing for dynamic LoRA (Low-Rank Adaptation) models across distributed compute clusters. By continuously tracking which specific hardware nodes currently hold requested adapter weights in memory, the orchestrator directs traffic to maximize adapter affinity and eliminate redundant, high-latency storage I/O.
This affinity mechanism is fluidly balanced against real-time queue depths to prevent localized bottlenecks or "thundering herd" scenarios.