Chapter 9: Performance Optimization
Exam focus
- Quantization
- Pruning
- Mixed precision (FP16/BF16)
- GPU acceleration
- Batch optimization
- Inference optimization
- Model compression
Scope Bullet Explanations
- Quantization: Lower numeric precision to reduce latency/memory footprint.
- Pruning: Remove low-impact parameters for efficiency.
- Mixed precision: Use lower precision compute with stability safeguards.
- GPU acceleration: Map workloads to optimized hardware paths.
- Batch optimization: Balance queueing latency and throughput gain.
- Inference optimization: Runtime-level graph/kernel/engine tuning.
- Compression: Shrink model footprint for deployability.
Chapter overview
Performance is a first-class exam theme because multimodal systems are expensive and latency-sensitive. This chapter focuses on practical tradeoff management: speed gains without unacceptable quality loss.
Assumed foundational awareness
Expected baseline:
- latency vs throughput distinction,
- precision formats (FP32/FP16/BF16) awareness,
- basic GPU memory constraints.
Learning objectives
- Explain optimization methods and their tradeoffs.
- Choose precision and batching strategies for workload goals.
- Evaluate optimization impact on both quality and runtime.
- Build optimization plans tied to SLO and cost targets.
9.1 Model-level optimization techniques
Quantization
Benefits: lower memory and faster inference. Risks: potential quality regression, calibration challenges.
Pruning and compression
Benefits: smaller deployment artifact and potentially faster execution. Risks: structural imbalance and accuracy degradation if aggressive.
9.2 Numeric precision strategy
Mixed precision (FP16/BF16) is widely used for speed and memory efficiency. Stability depends on calibration and validation across realistic workloads.
9.3 Runtime and serving optimization
Key levers:
- optimized execution engines,
- kernel fusion and graph simplification,
- request batching,
- concurrency tuning,
- caching strategy.
Batch tuning requires careful queue management to avoid tail-latency spikes.
9.4 Optimization decision framework
- Define SLO and quality floor.
- Establish baseline latency/throughput/cost.
- Apply one optimization at a time.
- Validate quality regression risk.
- Keep rollback-ready deployment path.
9.5 Multimodal-specific optimization concerns
Different modalities have different compute hotspots. For example, vision encoders and audio preprocessing can dominate latency depending on pipeline design.
Optimize per-stage rather than only end-to-end averages.
Common failure modes
- Applying aggressive quantization without quality gates.
- Increasing batch size until P99 latency violates SLO.
- Focusing on mean latency while ignoring tail behavior.
- Deploying optimized models without rollback artifacts.
Chapter summary
Optimization is controlled tradeoff engineering. Exam-ready reasoning requires connecting method choice to explicit runtime and quality targets.
Mini-lab: optimization runbook
- Capture baseline serving metrics.
- Apply mixed precision or quantization.
- Re-measure latency, throughput, and quality.
- Decide accept/reject based on pre-defined gates.
Deliverable:
- optimization comparison sheet with recommendation.
Review questions
- Why can quantization improve throughput but hurt quality?
- When is pruning likely to be counterproductive?
- How does mixed precision improve performance?
- Why must P95/P99 be tracked in production?
- What is one risk of over-batching?
- How should rollback be designed for optimization deployments?
- Why is per-stage profiling important in multimodal systems?
- What does model compression change operationally?
- How do you define acceptable quality regression?
- Why should optimization experiments be isolated and incremental?
Key terms
Quantization, pruning, mixed precision, P99 latency, batching, engine optimization, model compression.
Exam traps
- Treating optimization as latency-only work.
- Ignoring quality drift after precision changes.
- Using average metrics as the only acceptance criterion.