Performance Analysis of Gemma 4 E4B: Stress Testing 128K Context Window on Consumer GPU
A recent benchmark evaluated the Gemma 4 E4B model's ability to handle a 128K context window using an RTX 5050 laptop GPU. The testing revealed a strong capability in retrieval tasks (high recall) but highlighted significant performance bottlenecks in the initial token generation phase (prefill latency).
Methodology and Hardware Setup
The evaluation focused on stress-testing the expansive 128K context window capability of the Gemma 4 E4B model. The benchmark was executed on consumer hardware—specifically, an RTX 5050 laptop GPU. The objective was to quantify the trade-offs between context depth and real-world inference performance.
The test involved running a specific benchmark across four different context sizes, utilizing a "needle-in-a-haystack" query methodology. This process required the model to locate and retrieve specific information embedded within a large volume of input data.