8.3 Model Optimization¶
Role at Stage 8: Optimization and Hardware Acceleration¶
Change or approximate computation while measuring quality risk. 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>8.3</b><br/>Model Optimization"]
P --> S1["<b>8.3.1</b><br/>Quantization Formats"]
P --> S2["<b>8.3.2</b><br/>Calibration and<br/>Quality Checks"]
P --> S3["<b>8.3.3</b><br/>Distillation and<br/>Pruning"]
P --> S4["<b>8.3.4</b><br/>Speculative Decoding"]
P --> E["<b>Exam</b><br/>Part practice"]Sub-Parts¶
| Sub-part folder | What it explains |
|---|---|
| 8.3.1 Quantization Formats | Quantization Formats is the working skill inside Model Optimization 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. |
| 8.3.2 Calibration and Quality Checks | Calibration and Quality Checks is the working skill inside Model Optimization 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. |
| 8.3.3 Distillation and Pruning | Distillation and Pruning is the working skill inside Model Optimization 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. |
| 8.3.4 Speculative Decoding | Speculative Decoding is the working skill inside Model Optimization 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 Model Optimization supports an inference benchmark and optimization report for an open-weight or hosted model workload..
- Build and inspect this artifact: Compare baseline and optimized model variants.
- Measure progress with: Track memory reduction, speedup, eval drop, validity, and failures.
- Debug at least one failure mode before moving to the next part.
Build and Measure¶
Build: Compare baseline and optimized model variants.
Measure: Track memory reduction, speedup, eval drop, validity, and failures.
Tests¶
Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.