8.1 Inference Performance Model¶
Role at Stage 8: Optimization and Hardware Acceleration¶
Use the measurement vocabulary and workload model behind optimization. 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>8.1</b><br/>Inference Performance<br/>Model"]
P --> S1["<b>8.1.1</b><br/>Latency Throughput and<br/>Cost Metrics"]
P --> S2["<b>8.1.2</b><br/>Prefill Decode and KV<br/>Cache"]
P --> S3["<b>8.1.3</b><br/>Batch Shape and<br/>Concurrency"]
P --> S4["<b>8.1.4</b><br/>Benchmark Design"]
P --> E["<b>Exam</b><br/>Part practice"]Sub-Parts¶
| Sub-part folder | What it explains |
|---|---|
| 8.1.1 Latency Throughput and Cost Metrics | Latency Throughput and Cost Metrics is the working skill inside Inference Performance Model 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. |
| 8.1.2 Prefill Decode and KV Cache | Prefill Decode and KV Cache is the working skill inside Inference Performance Model 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. |
| 8.1.3 Batch Shape and Concurrency | Batch Shape and Concurrency is the working skill inside Inference Performance Model 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. |
| 8.1.4 Benchmark Design | Benchmark Design is the working skill inside Inference Performance Model 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 Inference Performance Model supports an inference benchmark and optimization report for an open-weight or hosted model workload..
- Build and inspect this artifact: Benchmark varied prompt, output, and batch shapes.
- Measure progress with: Track TTFT, TPOT, throughput, p95, memory, and quality.
- Debug at least one failure mode before moving to the next part.
Build and Measure¶
Build: Benchmark varied prompt, output, and batch shapes.
Measure: Track TTFT, TPOT, throughput, p95, memory, and quality.
Tests¶
Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.