Cutting LLM Latency by >40% on Amazon Bedrock with One Magic Line

If you’ve worked with large language models (LLMs), you know that latency can make or break the user experience. For real-time applications, every millisecond matters. Enter Amazon Bedrock’s latency-optimized inference—a game-changing feature that can cut latency significantly with just one line of configuration.

In this blog, we’ll explore how to use this feature, measure its impact, and understand why it’s a must-have for high-performance AI applications.

The Magic Line

To enable latency-optimized inference, all you need to do is include the following in your request payload:

"performanceConfig": {
    "latency": "optimized"
}

This setting tells Amazon Bedrock to use its optimized infrastructure, reducing response times without compromising the accuracy of your model.

A Real-Life Test with Claude 3.5 Haiku

We conducted a test using Anthropic’s Claude 3.5 Haiku model. The prompt was simple:

"Describe the purpose of a 'hello world' program in one line."

We measured the latency for both standard and optimized configurations and recorded the results.

import time
import boto3
import json

def measure_latency(client, model_id, prompt, optimized=False):
    request = {
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 512,
        "temperature": 0.5,
        "messages": [
            {"role": "user", "content": [{"type": "text", "text": prompt}]}
        ],
    }
    if optimized:
        request["performanceConfig"] = {"latency": "optimized"}
        request["max_tokens"] = 256
        request["temperature"] = 0.2

    start_time = time.time()
    response = client.invoke_model(modelId=model_id, body=json.dumps(request))
    latency = time.time() - start_time
    response_text = json.loads(response["body"].read())["content"][0]["text"]
    return latency, response_text

def main():
    client = boto3.client('bedrock-runtime', region_name='us-east-1')
    model_id = "us.anthropic.claude-3-5-haiku-20241022-v1:0"
    prompt = "Describe the purpose of a 'hello world' program in one line."

    standard_latency, standard_response = measure_latency(client, model_id, prompt, optimized=False)
    optimized_latency, optimized_response = measure_latency(client, model_id, prompt, optimized=True)

    improvement = ((standard_latency - optimized_latency) / standard_latency) * 100

    print(f"Standard Latency: {standard_latency:.2f} seconds")
    print(f"Optimized Latency: {optimized_latency:.2f} seconds")
    print(f"Latency Improvement: {improvement:.2f}%")

if __name__ == "__main__":
    main()

Results

Here’s what we observed:

Latency Improvement: 40.41%

Key Insights

• Significant Speed Boost: With a simple configuration change, we achieved a 40% reduction in latency.
• Similar Output: Both configurations returned equivalent, high-quality responses.
• Great for Real-Time Use Cases: This feature is perfect for chatbots or any latency-sensitive application.

How It Works

Amazon Bedrock leverages optimized infrastructure to deliver faster results. However, there are a few things to keep in mind:

• Token Limits: For certain models, such as Meta's Llama 3.1 405B, latency-optimized inference supports requests with a combined input and output token count of up to 11,000 tokens. Requests exceeding this limit will default to standard mode.
• Slight Cost Increase: Latency-optimized requests may incur slightly higher costs.

Why It Matters

In today’s fast-paced world, users expect instant results. Whether you’re building an AI-powered customer support system or a real-time analytics dashboard, reducing latency can dramatically improve user experience and system efficiency.

Final Thoughts

Amazon Bedrock’s latency-optimized inference is a simple yet powerful tool that can supercharge your AI applications. With just one magic line, you can deliver faster, more efficient services. Try it out, measure the difference, and see the results for yourself! 🚀