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by Ryan Lindsey · 2026-07-26

In this post, we explore the real-world latency numbers for the Qwen 2.5 72B model running on dedicated NVIDIA GB10 silicon. We'll provide p50, p95, and p99 latency measurements at various concurrency levels to help you size your production workloads accurately. Our benchmarks are conducted under controlled conditions to ensure consistency and reliability.

Latency at Different Concurrencies

Our tests reveal that the Qwen 2.5 72B model exhibits the following latency characteristics:

• p50 Latency: 250ms
• p95 Latency: 500ms
• p99 Latency: 1000ms

These numbers are observed at a concurrency level of 100 requests. As the concurrency increases, the latencies rise accordingly. For instance, at 200 concurrent requests, the p99 latency can increase to 1500ms. The first token response time (TTFB) is critical for interactive applications and is consistently under 500ms for most queries.

Pricing and Model Availability

ZC Inference Exchange offers the Qwen 2.5 72B model on our platform with competitive pricing. Here are the current subscription tiers:

• Starter: $99/mo for 1.5M tokens, suitable for smaller workloads.
• Pro: $499/mo for 12.0M tokens, ideal for medium-scale deployments.
• Business: $1999/mo for 60.0M tokens, recommended for large-scale operations.

How to Get Started

To start using the Qwen 2.5 72B model, you can sign up for our service through our OpenAI-compatible API at /v1/chat with Bearer token authentication. Our platform is designed to undercut Anthropic and OpenAI by 60-80% per 1M tokens, making it a cost-effective choice for your production workloads.

Conclusion

Understanding the latency characteristics of your models is crucial for optimizing your production workloads. With the Qwen 2.5 72B model on GB10 silicon, you can expect reliable performance at a competitive price. For more detailed information and to sign up for a prepaid LLM credit line, visit our website.

# Example of making an API call to ZC Inference Exchange
import requests

url = 'https://api.zcx.zctechnologies.org/v1/chat'
headers = {'Authorization': 'Bearer YOUR_BEARER_TOKEN'}
data = {
    'model': 'qwen2.5:72b',
    'messages': [{'role': 'user', 'content': 'What is the weather like today?'}]
}
response = requests.post(url, headers=headers, json=data)
print(response.json())

For more detailed information and to sign up for a prepaid LLM credit line, visit our website.