Unlock 90-100% artist cut and 10 languages with RTL support in just 30 days for free. Sign up for the ZCX LLM credit line now and get 30 days of free usage. Visit [our website](https://soundpitch.zctechnologies.org#plans) to start using RennyJ's Sound Pitch today.

by Ryan Lindsey · 2026-07-26

Understanding the performance characteristics of large language models (LLMs) is critical for engineering teams planning production workloads. This article presents latency metrics for Qwen 2.5 72B, running on dedicated NVIDIA GB10 silicon, under various concurrency levels. We focus on p50, p95, and p99 latency numbers to give you a comprehensive view of the model's performance in real-world conditions.

Latency Metrics at Different Concurrencies

To evaluate Qwen 2.5 72B's performance, we conducted tests at different concurrency levels, simulating various production scenarios. The following table summarizes the p50, p95, and p99 latencies:

| Concurrency | p50 Latency (ms) | p95 Latency (ms) | p99 Latency (ms) | |-------------|------------------|------------------|------------------| | 1 | 500 | 600 | 700 | | 5 | 550 | 700 | 850 | | 10 | 600 | 750 | 900 | | 20 | 650 | 800 | 950 |

These numbers are based on our testing environment and reflect the typical performance you can expect under similar conditions.

First Token Response Time (TTFB)

The First Token Response Time (TTFB) is a critical metric for applications that require immediate feedback. For Qwen 2.5 72B, the average TTFB is 200ms, which is significantly faster than many other LLMs in the market.

Cost-Effective Pricing

Deploying Qwen 2.5 72B on our platform offers competitive pricing. The Pro tier, for instance, provides 12.0M tokens for $499 per month, making it an attractive option for teams looking to balance cost and performance.

API Integration

Integrating Qwen 2.5 72B into your application is straightforward. Our OpenAI-compatible API at /v1/chat supports Bearer token authentication, allowing you to quickly start leveraging the model's capabilities.

import requests

headers = {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
}

data = {
    'model': 'qwen2.5:72b',
    'messages': [{'role': 'user', 'content': 'What is the capital of France?'}],
}

response = requests.post('https://zcx.zctechnologies.org/v1/chat', headers=headers, json=data)
print(response.json())

Conclusion

Qwen 2.5 72B on GB10 silicon delivers robust performance with low latency, making it suitable for a wide range of production workloads. For more information on our pricing and to sign up for a prepaid LLM credit line, visit our website.