NewPre-deployment SLO estimation is now in preview —see it in action

Inference Operating System
for Token Factories

Take control of production inference. One layer to orchestrate, optimize, and govern AI workloads — across any model, any GPU, any cloud.

nexus · manifold
Models
Llama
DeepSeek
Qwen
GPT-OSS
Mistral
Kimi
Engines
vLLM
TensorRT
SGLang
GPUs
H100
MI300
TPU v5
on-prem
NR-NEXUS
Optimized to your preference
Trusted by
QualcommAMDMicrosoftCiscoARM

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 more

K8s-native orchestration

Deploy inference as Kubernetes-native workloads. No custom operators, no vendor lock-in.

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.

Results

Proven in production

Real customer outcomes from NR-NEXUS deployments. Figures confirmed in press release — flag for stakeholder review before publish.

15×
Concurrent sessions
GenAI · 64 → 981
3.3×
Tokens per GPU
GenAI · 2.5K → 8.2K
Sessions scaled
SaaS · 64 → 512
+32%
Faster interactive
44 → 58 tok/user

See it on your own workload.

One model. One week. Measure the cost and performance impact on your own infrastructure.