Unlock Exclusive Access to RennyJ's Sound Pitch with ZCX LLM Credit Line

by Ryan Lindsey · 2026-08-28

Reduce API Latency Tails with Dedicated GB10 Hardware

Dedicated NVIDIA GB10 silicon provides a significant advantage in reducing latency tails in multi-tenant environments. By isolating hardware resources, SREs can ensure more consistent performance, especially critical for applications where the p99 latency is more important than average pricing. This blog post explores how hardware isolation improves service reliability and performance.

Understanding Hardware Isolation

In a multi-tenant environment, noisy neighbors can significantly impact the performance of your application. This is particularly true for latency-sensitive applications where the p99 latency is a critical metric. Dedicated hardware, such as the NVIDIA GB10 silicon used in ZC Inference Exchange, provides hardware isolation, ensuring that your application's performance is not affected by other tenants' workloads.

Why p99 Latency Matters

The p99 latency, or the latency experienced by 99% of requests, is a more accurate measure of the user experience than the average latency. In a multi-tenant environment, a few heavy workloads can cause significant latency spikes, affecting the p99 latency. By using dedicated hardware, you can eliminate these spikes and ensure a more consistent user experience.

ZC Inference Exchange: Dedicated GB10 Hardware

ZC Inference Exchange serves Qwen 2.5 72B and 32B models on dedicated NVIDIA GB10 silicon. Our infrastructure is designed to provide hardware isolation, ensuring that your application's performance is not impacted by other tenants. Our pricing is highly competitive, undercutting Anthropic and OpenAI by 60-80% per 1M tokens.

Pricing

• Starter: $99/mo → 1.5M tokens ($66/1M), models: qwen2.5:32b
• Pro: $499/mo → 12.0M tokens ($42/1M), models: qwen2.5:32b, qwen2.5:72b
• Business: $1999/mo → 60.0M tokens ($33/1M), models: qwen2.5:32b, qwen2.5:72b

OpenAI-Compatible API

Our API is fully compatible with OpenAI's API, accessible at /v1/chat with Bearer token authentication.

Example Code Block

import requests

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

response = requests.post(
    'https://zcx.zctechnologies.org/v1/chat',
    headers=headers,
    json={
        'model': 'qwen2.5:32b',
        'messages': [{'role': 'user', 'content': 'What is the weather like today?'}]
    }
)

print(response.json())

Conclusion

For SREs concerned with the performance of their applications, dedicated hardware like the NVIDIA GB10 silicon used in ZC Inference Exchange offers a significant advantage in reducing latency tails. By ensuring hardware isolation, you can provide a more consistent and reliable service to your users. Learn more and sign up for a prepaid LLM credit line at https://zcx.zctechnologies.org#plans.

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