ARCHITECTURE_&_MODELS

Production-grade distributed systems and machine learning models. Built for high-throughput, low-latency execution environments.

PULL_REPO

Nexus_Event_Streamer

GOKAFKAK8S

A high-throughput, fault-tolerant event streaming platform designed to handle 5M+ events/sec. Utilizes custom Go channels and optimized Kafka partition routing for sub-millisecond latency.

Throughput

5.2M ev/s

P99 Latency

0.8ms

arch_diagram.mermaid

graph TD
    A[Client API] -->|gRPC| B(Ingress Router)
    B --> C{Partition Logic}
    C -->|Hash| D[Kafka Broker 1]
    C -->|Hash| E[Kafka Broker 2]
    C -->|Hash| F[Kafka Broker N]
    D --> G[Go Worker Pool]
    E --> G
    F --> G

Vision_Transformer_Lite

PYTORCHCUDAONNX

Quantized ViT model optimized for edge devices. Achieves near state-of-art accuracy with 80% reduction in parameter count.

> Model Size: 12MB

> Inference: 45fps (Jetson Nano)

> Status: Deployed_V1.2

Lattice_Feature_Store

RUSTREDISGRPC

Low-latency online feature store backing real-time inference. Point lookups served from a Rust core with write-through caching.

> Read p99: 1.4ms

> Features: 2,800+

> Status: Online

Atlas_Inference_Gateway

PYTHONTENSORRTK8S

Multi-tenant inference gateway with dynamic batching, autoscaling, and per-model routing. Cut GPU spend by 38% while holding latency SLOs under load.

GPU Savings

38%

Req Volume

120k rpm

routing.mermaid

graph LR
    A[Client] --> B(API Gateway)
    B --> C{Model Router}
    C -->|batch| D[Triton: GPU Pool]
    C -->|cpu| E[ONNX Runtime]
    D --> F[Result Cache]
    E --> F
    F --> A