> ## Documentation Index
> Fetch the complete documentation index at: https://vektorcompute-77d08130.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# The GPU Mesh Network

> Architecture of the decentralized inference routing layer.

The Vektor GPU Mesh is a peer-to-peer network of enterprise-grade compute nodes, specifically optimized for high-throughput AI inference and Large Language Model (LLM) serving.

### Architectural Overview

Unlike traditional cloud providers that rely on centralized, geographically isolated server farms, Vektor utilizes a **distributed mesh topology**. This ensures zero single points of failure and dynamic load balancing across global regions.

### The Routing Engine

When an AI developer submits an inference request to the Vektor API, the request is not routed to a static server. Instead, it hits the **Vektor Routing Engine**, an intelligent load balancer that evaluates the global mesh in milliseconds based on:

1. **Proximity:** Routing to the geographically closest available node to minimize latency to `< 50ms`.
2. **Hardware Matching:** Ensuring the specific model requirements (e.g., memory bandwidth, tensor core utilization) are matched with the optimal hardware (NVIDIA H100 vs. A100).
3. **Current Load:** Avoiding node saturation by distributing concurrent requests across multiple redundant data centers.

### Zero-Downtime Fallback

In the event of a localized power failure or hardware degradation at a specific data center, the mesh automatically reroutes the inference pipeline to the next optimal node within `12ms`. This guarantees the **99.97% Uptime SLA** promised to enterprise clients.