Latency is a critical concern for Site Reliability Engineers (SREs) managing multi-tenant environments. In such setups, the performance of one tenant can negatively impact others, leading to unpredictable latency spikes. By opting for dedicated hardware like the NVIDIA GB10, you can ensure consistent performance and eliminate the noisy neighbor effect. This post delves into the technical aspects of hardware isolation and why p99 latency is a more critical metric than average price for SREs concerned with service reliability.
In a multi-tenant environment, resources are shared among multiple users. While this can lead to cost savings, it also introduces the risk of noisy neighbors. A noisy neighbor is a tenant who consumes a disproportionate amount of resources, causing latency issues for other tenants. This is particularly problematic in machine learning inference workloads where consistent performance is crucial.
Dedicated hardware, such as the NVIDIA GB10 silicon used in ZC Inference Exchange, ensures that your workload has exclusive access to the hardware resources. This isolation means that your service's performance is not affected by the actions of other users. The GB10 silicon is specifically designed to handle the computational demands of large language models like Qwen 2.5, providing the necessary processing power for efficient inference.
When evaluating the performance of a system, it's important to look beyond the average latency. The p99 latency, which is the 99th percentile of latency, gives a more accurate picture of the worst-case scenario. In a multi-tenant environment, the p99 latency can be significantly higher than the average due to the noisy neighbor effect. By using dedicated hardware, you can ensure that your p99 latency is consistent and low, providing a more reliable service.
ZC Inference Exchange offers three plans to cater to different needs:
These plans provide a cost-effective way to access dedicated GB10 hardware and ensure that your service is not impacted by noisy neighbors.
For SREs looking to improve the reliability of their machine learning inference workloads, dedicated hardware like the NVIDIA GB10 is a critical component. By eliminating the noisy neighbor effect and focusing on p99 latency, you can ensure a more consistent and reliable service. To learn more about how ZC Inference Exchange can help, visit our plans page.
# Example API call to ZC Inference Exchange
curl -X POST https://zcx.zctechnologies.org/v1/chat \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer YOUR_BEARER_TOKEN' \
-d '{"model": "qwen2.5:32b", "messages": [{"role": "user", "content": "Hello, how can I help you today?"}]}'