Winning the Race

In GenAI, performance is a game of two halves: Latency (how fast the engine responds) and Throughput (how many engines you have running at once). For a professional application, a 10-second wait for a chatbot response is considered a failure.

In this lesson, we will master the engineering techniques to reduce Time to First Token (TTFT) and ensure your application remains responsive under heavy load.

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1. Metrics that Matter

• TTFT (Time to First Token): The time it takes for the first word to appear on the screen. This is the most important metric for user satisfaction.
• TPM (Tokens Per Minute): Your total capacity.
• P99 Latency: The response time for the slowest 1% of your users.

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2. Streaming (The #1 Perception Hack)

As we learned in Module 6, human brains perceive a 5-second wait as "slow" but a 0.5-second wait for the start of a sentences as "instant."

Pro Technique: Always use invoke_model_with_response_stream. It doesn't make the model generation faster, but it makes the App feel significantly more responsive.

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3. Provisioned Throughput (Guaranteed Speed)

On-demand Bedrock usage is like "Flying Standby." If the region is busy (high traffic), you might get throttled.

Provisioned Throughput is like "Renting a Private Jet."

• You reserve a specific amount of throughput for your model.
• You get a consistent, low latency regardless of how many other AWS customers are using Bedrock.
• Exam Tip: Use this for production-level critical apps with high, predictable traffic.

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4. Hardware Acceleration: AWS Inferentia

If you are hosting custom models on Amazon SageMaker, you can choose your hardware.

• Standard GPUs (NVIDIA): Great for general use and training.
• AWS Inferentia (Inf2 Instances): Purpose-built by AWS specifically for deep learning inference.
• Benefit: Up to 40% lower cost-per-inference and higher throughput than comparable GPU instances.

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5. Architectural Optimizations

Prompt Pruning

The model has to "read" every token in your prompt before it starts generating. A 100,000-token prompt has higher latency than a 1,000-token prompt.

• Action: Use Claude Prompt Caching (a new feature) or keep your prompts as short as possible to reduce "Prefill" time.

Parallel Processing

If you need to summarize 10 documents, don't do it sequentially.

• Action: Use AWS Lambda to trigger 10 parallel calls to Bedrock.

graph LR
    User[Large Task] --> L[Lambda Orchestrator]
    L --> C1[Call 1]
    L --> C2[Call 2]
    L --> C3[Call 3]
    C1 --> R[Aggregator]
    C2 --> R
    C3 --> R
    R --> User
    
    style L fill:#e1f5fe,stroke:#01579b
    style R fill:#e1f5fe,stroke:#01579b

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6. Multi-Region Proximity

If your users are in Tokyo but your model is in N. Virginia, you are adding 200ms of "Speed of Light" latency.

• Action: Deploy your application logic and models in regional pairs (e.g., us-east-1 for US users, eu-central-1 for EU users).

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Knowledge Check: Test Your Performance Knowledge

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Summary

Latency is the "Ghost in the Machine." By using Streaming, Provisioned Throughput, and Parallelism, you build applications that feel "Alive." In the final lesson of Module 14, we look at Performance Testing and Benchmarking.

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Next Lesson: Scientific Verification: Performance Testing and Benchmarking