Benchmark Analysis of INT4 Quantization Deployment for 9B-20B Models

On 2026-11-06 08:16, Juejin published an article titled "INT4 Quantization Deployment Benchmarks for 9B–20B Models: Performance Boundaries Behind the Speedup". Three independent researchers conducted hands-on benchmarks of INT4 quantization deployment for open-source models ranging from 9B to 20B on vLLM and SGLang. Their survey compiled every verifiable quantization benchmark for these models on those serving frameworks. The article reports that INT4 can deliver a 2x speedup in specific scenarios, yet it is prone to noticeable performance degradation and degradation-driven accuracy loss when the batch size grows. This is the earliest verifiable report on the matter, and the event has so far progressed only to this point.