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
Deploy an OpenClaw AI Agent (ClawdBot) on VectorLay in Minutes
Run your own private OpenClaw agent (ClawdBot) on an isolated VM. Choose CPU or GPU, connect to Signal, Telegram, or WhatsApp, and only pay for what you use.
Next-Gen GPUs Explained: H200, GB200, B200, MI300X for AI Inference
A complete guide to NVIDIA H200, GB200 NVL72, B200, and AMD MI300X GPUs. Specs, pricing, availability, and when each GPU makes sense for your AI workloads.
The Environmental Case for Distributed GPU Computing
Why reusing existing consumer GPUs for AI inference is greener than building new data centers. The environmental argument for distributed networks.
Kimi K2.5: The Open-Source Model That's Beating GPT-5.2 — And How to Host It
Moonshot AI's Kimi K2.5 is a 1T parameter open-source model outperforming closed-source giants on key benchmarks. Here's everything you need to know about deploying it on your own GPU infrastructure.
Best GPU Cloud for LLM Inference in 2026: Complete Guide
Compare the top GPU cloud providers for LLM inference. Side-by-side analysis of VectorLay, RunPod, Vast.ai, Lambda, AWS, and GCP for models from 7B to 70B parameters.
How to Reduce LLM Inference Costs by 80% in 2026
Practical strategies to cut your GPU inference bill — from right-sizing GPUs and quantization to distributed inference on consumer hardware.
Distributed GPU Inference Explained: How Overlay Networks Power Fault-Tolerant AI
How distributed GPU inference works, why overlay networks enable automatic failover, and how VectorLay built a fault-tolerant inference platform on consumer hardware.
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 2025: 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.
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