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
In three stages
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
Pick a workload and the service level you owe your users. NR-NEXUS sizes it against the hardware you already have — engine, parallelism, replicas, expected tokens per second — and 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 layer
Replace the fragmented tangle of serving engines, custom operators, and hand-rolled observability with a single production inference plane.
Intelligent routing
Every request finds its optimal path — engine selection, KV-aware routing, and disaggregation, out of the box.
Learn moreK8s-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 based on real-time workload signals — not static thresholds.
Observability
Token-level metrics, SLO dashboards, and per-tenant cost tracking built in from day one.
Lifecycle management
Canary rollouts, model versioning, and traffic shifting across model revisions without downtime.
Security
Tenant isolation, mTLS, audit logs, and RBAC — governed inference for regulated workloads.
GPT-OSS
Qwen
deepseek
LLaMA
Kimi
Results
Proven in production
Real customer outcomes from NR-NEXUS deployments. Figures confirmed in press release — flag for stakeholder review before publish.
Models
The models your teams already use
Serve them on the hardware you have, and swap models later without re-architecting.
Solutions
One platform. Two 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.
One model. One week. Measure the cost and performance impact on your own infrastructure.