High-performance, horizontally scalable AI infrastructure built for real-time inference, continuous learning, and enterprise-grade reliability.
gRPC / REST • Auth • Rate Limiting
K8s • Service Mesh • Load Balancing
TensorRT • vLLM • Dynamic Batching • KV Cache
Versioned Artifacts • A/B Routing • Shadow Testing
mTLS • PII Redaction • Audit Logging
Prometheus • OpenTelemetry • Drift Detection
Optimized tensor execution with dynamic shape support and speculative decoding for LLMs.
Real-time feature store with exactly-once semantics and automated schema evolution.
Hardware-rooted identity, policy-as-code enforcement, and automated compliance scanning.
End-to-end tracing with ML-specific metrics: token utilization, KV cache hit rate, drift alerts.
| Layer | Technology | Purpose | Deployment |
|---|---|---|---|
| Compute | NVIDIA H100 / A100 | GPU-accelerated inference | On-Prem / Cloud |
| Orchestration | Kubernetes (k3s / EKS) | Container scheduling & scaling | Multi-region |
| Model Serving | vLLM + TensorRT-LLM | High-throughput LLM deployment | GPU Nodes |
| Data Pipeline | Apache Flink + Iceberg | Streaming features & lakehouse | Managed |
| Vector DB | Milvus / Qdrant | Embedding retrieval & RAG | StatefulSets |
| API Transport | gRPC + Protobuf | Low-latency inter-service comms | Sidecar Proxies |
| CI/CD | GitLab CI + ArgoCD | GitOps model deployment | Cluster-wide |
# Initialize NexusAI Client
from nexusai import Client
client = Client(api_key="nx_live_...", region="us-east-1")
# Stream inference with dynamic routing
async for chunk in client.models.stream(
model="nexus-v3-ultra",
prompt="Analyze system architecture...",
options={
"router": "latency-optimized",
"cache_mode": "semantic"
}
):
print(chunk.tokens, end="", flush=True)