Model Serving

High-throughput LLM hosting, KV caching, vLLM, speculative decoding, and quantization.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Model serving capacity and bottlenecks
Build a benchmarked inference endpoint
Architect reliable multi-model serving

Helpful before starting

Model inference and tensor shapes
HTTP/RPC services and queues
GPU memory, concurrency, and observability basics

Start here

Model Serving, in plain language

High-throughput LLM hosting, KV caching, vLLM, speculative decoding, and quantization. Serving determines whether model quality can be delivered within latency, throughput, reliability, and cost constraints under real traffic.

For a small example, three inputs arrive within a short window. Pad or pack them, run one forward pass, split outputs, and compare throughput with added queue delay. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Request lifecycle, preprocessing, inference, postprocessing, and response.
Latency percentiles, throughput, concurrency, utilization, and queueing.
Batch vs online, CPU vs GPU, and stateless vs stateful serving.

Use it well

When Model Serving helps—and where it breaks

A classifier must respond under a 100 ms target. Profile preprocessing and model time, add bounded batching, validate schemas, load-test tails, and canary a version. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Reporting average latency only. Better approach: Use percentiles and inspect queueing under burst load.
Autoscaling on utilization alone. Better approach: Include queue depth, request shape, and startup delay.
Deploying a model without overload policy. Better approach: Define admission, prioritization, degradation, and shedding behavior.

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