Hardware isolation provided by dedicated GB10 silicon eliminates the noisy neighbor effect, ensuring consistent performance for your machine learning workloads. This post delves into why hardware isolation is crucial for maintaining low latency, especially in the 99th percentile, and how it impacts your infrastructure's reliability.
In multi-tenant environments, the performance of your workload can be significantly impacted by the activities of other tenants. This is known as the noisy neighbor problem. When another tenant's workload spikes, it can consume a disproportionate amount of shared resources, leading to increased latency for all users. This is particularly problematic for critical operations where consistency is paramount.
Dedicated GB10 silicon ensures that your workload runs on hardware exclusively allocated to your account, eliminating the noisy neighbor effect. This isolation guarantees that your machine learning tasks have consistent access to the necessary computational resources, leading to more predictable performance.
Average latency is often cited as a key performance indicator, but it can mask significant issues in the tail of the distribution. The 99th percentile (p99) latency is a more accurate measure of the worst-case scenario that your users might experience. In a multi-tenant environment, p99 latency can be much higher than the average, due to the noisy neighbor effect. With dedicated hardware, you can ensure that even in the worst-case scenarios, your latency remains within acceptable bounds.
Our pricing plans offer competitive rates, undercutting Anthropic and OpenAI by 60-80% per 1M tokens. Here are the details:
Our ZC Inference Exchange serves Qwen 2.5 72B (frontier) and 32B models on dedicated NVIDIA GB10 silicon. The API is compatible with the OpenAI API at /v1/chat with Bearer token authentication. Here's a sample request:
$ curl https://zcx.zctechnologies.org/v1/chat \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "qwen2.5:72b", "messages": [{"role": "user", "content": "What is the weather like today?"}]}'For SREs looking to maintain high performance and reliability in their machine learning workloads, dedicated GB10 silicon is a critical investment. It ensures that your infrastructure can handle peak loads without the unpredictable performance degradation associated with multi-tenant environments. For more details on our plans and to sign up, visit our website.