Assess the entire inference stack — not just open model benchmarks
Impressive benchmark results should make it easy for engineers to select the right open model for the outcome they are The post Assess the entire inference stack — not just open model benchmarks appeared first on The New Stack .
Benchmark results for open models may indicate their optimal performance, but that doesn't guarantee accurate, efficient, or effective inference in production. Factors such as provided information, accuracy, and long-horizon AI tasks matter more in real-world scenarios. Ryan Hanrui Wang, SVP of Applied AI at AI cloud provider Nebius, emphasizes the importance of considering the entire inference stack, including internal benchmarks and specific customer workflows, to ensure desired outcomes.
Nebius evaluates open models like Kimi and DeepSeek for lower-cost options and fine-tunes larger open models for improved accuracy. Model selection should depend on whether a lower-cost, flexible stack or a frontier model for inference is needed. Engineers must balance token cost with acceptable interactivity and watch tail latency, not just averages, to ensure a good user experience.
Selecting the right-sized open model for the specific AI task is crucial, as accuracy gains from increasing model size diminish after a certain point. Nebius uses 6,000 traces to refine small models and improve accuracy while reducing tool calls by 22%, demonstrating that bigger isn't always better.
Written by urgent.news from The New Stack's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
