Interactive lesson~18 minIntermediate
Model Serving & Deployment
Model serving turns trained weights into reliable products. The system must batch, cache, stream, monitor, and recover under real traffic.
vLLMTensorRTContinuous batching
Mental model
Serving is logistics for intelligence.
Latency, cost, throughput, and reliability often decide whether an AI product works in practice.
Latency
balanced60% modeled signal
Throughput
balanced65% modeled signal
Cost control
balanced54% modeled signal
Concept pipeline
Build the idea in four moves
Interactive lab
Serve a chat model under spiky traffic.
Load
Place weights on the right hardware.
Focus lens
The part that usually clicks late
Continuous batching
Keep accelerators busy as requests arrive and finish.
Latency
60
Throughput
65
Cost control
54
Knowledge check
What does continuous batching improve?
Next horizon
Where this topic is headed
vLLM paged attention
Speculative serving
SLO-aware routing