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🖥️ Model Serving Architectures for Production ML

A 3D inference cluster where requests flow through a load balancer to model replicas, showing how request rate, batch size and autoscaling trade off latency, throughput and queue depth.

AI & Machine Learning3DAdvanced60 FPS

A 3D inference cluster where requests stream from clients through a load balancer to a pool of model replicas, showing how traffic, batching and autoscaling shape real production latency and throughput.

🔬 What It Demonstrates

As utilization climbs toward full capacity, queue depth and latency rise sharply — the classic queueing "hockey stick" every serving system has to design around, alongside the throughput/latency trade-off of dynamic batching.

🎮 How to Use

Raise the request rate and watch replicas turn red as they saturate. Increase batch size to see throughput per replica rise but tail latency grow. Toggle autoscaling to compare a fixed-size deployment against one that reacts to load.

💡 Did You Know?

Production inference services like NVIDIA Triton and TorchServe use dynamic batching windows measured in single-digit milliseconds — just enough to group concurrent GPU requests without meaningfully hurting response time.

⚙ Under the hood

A 3D inference cluster where requests flow through a load balancer to model replicas, showing how request rate, batch size and autoscaling trade off latency, throughput and queue depth.

machine learningartificial intelligencemodel servinginferenceproduction mlThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install