The Inference Operating System
for Production AI
Take control of production inference. One layer to orchestrate, optimize, and govern AI workloads across the models, accelerators and clouds you already run.
How it works
Plan, deploy, observe
Size a deployment against the hardware you already have, bring it up without writing infrastructure, and watch every token — cost included. Pick a workload below to see what the planner returns.
Size it before you buy it
NR-NEXUS™ sizes it against the hardware you already have: engine, parallelism, replicas, expected tokens per second. It tells you when the plan cannot hit your targets.
You choose a workload
NR-NEXUS™ returns a sized plan
No YAML, no operators
NR-NEXUS™ checks the cluster, reserves the capacity, stages the weights and brings the endpoint up. Nothing to hand-write, and nothing left half-deployed.
- Cluster checkedaccess, storage and capacity confirmed
- Capacity reserved8 accelerators held for this plan
- Model stagedweights pulled and verified
- Endpoint liveserving traffic behind your SLO
Miss a resource — capacity short, a node offline — and the plan stops right there and names it, before anything is charged.
Every token accounted for
Token-level telemetry from the moment traffic lands — latency, throughput and cost in one place, built from widgets so each team sees what it needs.
Start a PoCThat is the whole job.
One plan, one endpoint, every token measured — on hardware you already own.
Why NR-NEXUS™
One unified operating system
Replace a fragmented stack of serving engines, custom operators, and hand-rolled observability with one production inference plane.
Intelligent routing
Engine selection, KV-aware routing, and disaggregation, applied per request, out of the box.
K8s-native orchestration
Deploy inference as Kubernetes-native workloads — no custom operators to build or maintain.
AI-aware scaling
Scale to zero or to peak demand on live workload signals instead of fixed thresholds.
Observability
Token-level metrics, SLO dashboards, and per-tenant cost tracking.
Lifecycle management
Canary rollouts, model versioning, and traffic shifting across model revisions without downtime.
Security
Tenant isolation, mTLS, audit logs, and RBAC for regulated workloads.
Bring your workload. We'll size it against your hardware.
GPT-OSS
Qwen
deepseek
LLaMA
Kimi
Results
Real outcomes from NR-NEXUS™ deployments
Before and after on the same GPU fleet. No hardware added.
Models
Serve the right model for the right use case
Deploy on the hardware you have, and swap models later without re-architecting.
LlamaDeepSeekQwenKimi
MistralGPT-OSS
Gemma
Phi
GLM
Nemotron
MiniMaxSolutions
Paths to Production
For Enterprise
Take control of your AI economics. Govern inference at scale with full cost visibility, SLO enforcement, and multi-model serving, without a dedicated infrastructure team.
Explore EnterpriseFor NeoClouds
Turn your GPU infrastructure into managed token factories. Monetize idle capacity, differentiate beyond raw compute, and deliver managed inference at hyperscaler margins.
Explore NeoCloudsSee it on your own workload.
Measure the cost and performance impact on your own infrastructure.