8.4 Serving Engines¶
Role at Stage 8: Optimization and Hardware Acceleration¶
Understand how runtimes schedule, batch, stream, and manage model memory. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.
Explanation¶
This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.
Part Diagram¶
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flowchart LR
P["<b>8.4</b><br/>Serving Engines"]
P --> S1["<b>8.4.1</b><br/>vLLM SGLang TGI and<br/>TensorRT LLM"]
P --> S2["<b>8.4.2</b><br/>Continuous Batching"]
P --> S3["<b>8.4.3</b><br/>Paged Attention"]
P --> S4["<b>8.4.4</b><br/>Streaming and<br/>Scheduler Policy"]
P --> E["<b>Exam</b><br/>Part practice"]Sub-Parts¶
| Sub-part folder | What it explains |
|---|---|
| 8.4.1 vLLM SGLang TGI and TensorRT LLM | vLLM SGLang TGI and TensorRT LLM is the working skill inside Serving Engines that helps you build the stage artifact, An inference benchmark and optimization report for an open-weight or hosted model workload, while collecting enough evidence to trust the result. |
| 8.4.2 Continuous Batching | Continuous Batching is the working skill inside Serving Engines that helps you build the stage artifact, An inference benchmark and optimization report for an open-weight or hosted model workload, while collecting enough evidence to trust the result. |
| 8.4.3 Paged Attention | Paged Attention is the working skill inside Serving Engines that helps you build the stage artifact, An inference benchmark and optimization report for an open-weight or hosted model workload, while collecting enough evidence to trust the result. |
| 8.4.4 Streaming and Scheduler Policy | Streaming and Scheduler Policy is the working skill inside Serving Engines that helps you build the stage artifact, An inference benchmark and optimization report for an open-weight or hosted model workload, while collecting enough evidence to trust the result. |
What a Person Who Masters This Part Can Do¶
- Explain how Serving Engines supports an inference benchmark and optimization report for an open-weight or hosted model workload..
- Build and inspect this artifact: Serve a model or design a serving plan.
- Measure progress with: Track throughput, p95, memory, utilization, errors, and cost.
- Debug at least one failure mode before moving to the next part.
Build and Measure¶
Build: Serve a model or design a serving plan.
Measure: Track throughput, p95, memory, utilization, errors, and cost.
Tests¶
Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.