Nexus Compute

AI Inference Cluster

High-availability infrastructure for serving AI models to production at scale.

Overview

The AI Inference Cluster is engineered for serving models reliably to many concurrent users. Where a training cluster optimizes for raw throughput, an inference cluster optimizes for latency, availability, and cost-per-request. Nexus Compute designs and sources the right balance of GPUs, networking, and redundancy for your serving workload. Inference sizing works backwards from the response you have promised. Half-precision weights need roughly 2GB per billion parameters before any KV cache, and it is the cache that grows with concurrency and context length, so the memory question is usually about how many simultaneous requests you must hold, not about the model alone. Once each replica fits within a node the cluster scales horizontally and the interconnect matters far less than it does in training; where a model does not fit, it is split across GPUs and the intra-node link becomes a latency term in every request. Redundancy is the other axis: serving to customers means holding enough capacity for a node to fail or be patched without breaching the latency target, so the cluster is sized above peak rather than at it.

Warranty & Support