Building the future of
distributed inference
Deep dives into our architecture, engineering decisions, and the technology powering Vectorlay's fault-tolerant GPU network.
Architecture Deep Dive Series
5 partsThe Control Plane: WebSockets, Registration, and Job Queues
How Vectorlay's control plane coordinates thousands of GPU nodes with WebSockets, zero-touch provisioning, and reliable job delivery via BullMQ.
The Agent: Node Software, Heartbeats, and Container Management
How the agent runs on GPU nodes, manages dependencies, reports health, and executes container deployments with Kata Containers.
GPU Passthrough with Kata Containers
How we use VFIO and Kata Containers to provide direct GPU access with VM-level isolation for untrusted workloads.
Fault Tolerance: Health Checks, Failover, and Self-Healing
How Vectorlay detects failures, routes around unhealthy nodes, and automatically recovers workloads without manual intervention.
More Articles
Why We Keep Container Deployments Simple (And You Should Too)
Vectorlay deliberately chose a simple 'one container per cluster' model over complex multi-container orchestration. This isn't a limitation—it's a feature. Here's why simplicity wins for GPU inference.
How to Make Money from Your Gaming GPU
Turn your idle RTX 4090 or 3090 into a passive income stream. Learn how to rent out your GPU for AI inference and earn $300+/month while you sleep.
The Complete Guide to Becoming a Vectorlay Provider
Step-by-step technical guide to setting up your GPU node. From BIOS configuration to VFIO passthrough to going live on the network.
GPU Cloud Pricing Comparison 2024: Vectorlay vs AWS vs GCP vs RunPod
Side-by-side comparison of GPU cloud pricing for ML inference. See how Vectorlay saves you 50-80% compared to AWS, Google Cloud, and other providers.
Deploy Your First Model on VectorLay
A step-by-step guide to deploying your first ML model on VectorLay's distributed GPU network in under 10 minutes.
Running Stable Diffusion XL at Scale
Deploy Stable Diffusion XL on distributed GPUs for high-throughput image generation. Includes benchmarks, code examples, and cost analysis.
LLM Inference at Scale with VectorLay
Deploy Llama, Mistral, and other open-source LLMs at scale. Benchmarks, cost analysis, and production deployment patterns.
Real-Time AI Inference: Building Low-Latency Applications
Build real-time AI features with sub-100ms latency. Covers architecture patterns, edge deployment, and optimization techniques.
Deploy Self-Hosted GitHub Actions Runners on Vectorlay
Run GitHub Actions on your own infrastructure for faster builds, no queue times, and GPU access. Step-by-step guide with troubleshooting tips.
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