I stress-tested Gemma 4 E4B's 128K context on a laptop GPU — recall is great, prefill is not
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Stress-testing Gemma 4's 128K context on laptop GPU provides concrete benchmarks for local LLM deployment.
Stress-testing Gemma 4 E4B (Q4_K_M, ~9.6 GB) on an RTX 5050 laptop with 8 GB VRAM showed perfect recall across 5K–100K context in a needle-in-a-haystack test, but time to first token (prefill) scaled nearly linearly from 4s at 5K to 72s at 100K, while generation throughput dropped only 26% (9.2→6.8 tok/s). The author defines three practical zones—interactive (<20K), research-assistant (20–60K), batch (60–100K)—and provides a ~30-line Python rig on Ollama 0.24.0 to reproduce the results.
Design your UI around prefill latency zones: interactive (<20K), research (20–60K), batch (60–100K) when using Gemma 4 E4B on laptop GPUs.
For a solutions architect building agentic systems or LLM-powered UIs, these latency numbers expose the prefill bottleneck on consumer GPUs, directly informing when to use synchronous vs. batch processing and how to surface context-size expectations to users.
Gemma 4 Challenge: Write about Gemma 4 Submission Thursday night I let a benchmark run while I slept. By Friday morning Gemma 4 E4B had answered fifteen needle-in-a-haystack questions across four context sizes on my RTX 5050 laptop. The recall numbers were better than I expected. The latency numbers were worse. Here's both, with the ~30 lines of Python to reproduce it on your own hardware. I keep seeing "Gemma 4 E4B has a 128K context window" repeated as if it were a single property, like "the engine is 3.5 litres"