Gemma 4 E4B-it Q4_K_M; KodeLab Chinese prose three-run average

Primary source: https://klab.tw/2026/06/gemma4-benchmark/ , KodeLab author Kyle, sections describing benchmark methodology and Mac mini M4 token speed. Page lists June 7, 2026 publication and August 13, 2026 update. Exact benchmark date is not given. Recovered October 13; no local reproduction performed. One Apple Mac mini M4 with 32 GB unified memory, Ollama 0.30.5, GGUF Q4_K_M, num_ctx=4096, source reports 100% GPU offload. Exact CPU/GPU core counts, macOS, power, clocks, thermals and storage are not reported. Do not borrow hardware details or flags from SymbolStar's independent M4 mini campaign. Measured model is Ollama library gemma4:e4b, obtained through ollama pull; source explicitly says its benchmarks use the library's GGUF Q4_K_M builds rather than downloaded HF originals or a self-made quant. Current tag identity verified at https://ollama.com/library/gemma4:e4b . Upstream model identity verified at https://huggingface.co/google/gemma-4-E4B-it . This HF link identifies the original E4B-it model, not the exact tested GGUF. The historical Ollama manifest/blob digest, tested model hash, upstream revision, quantizer and HF artifact-equivalent are unknown. Today's tag can contain newer weights, drafting and multiple runtime formats and must not substitute for the source's historical GGUF test. Headline is the source-reported three-run average for this model: native decode 29.87 tokens/s and native prompt processing 566 tokens/s. The speed test uses a fixed Chinese long-form prompt asking for an article of at least 400 Chinese characters. That 400-character instruction is not an actual output-token count. Both actual prompt/output counts, per-run measurements, unrounded timings, dispersion, output cap and mean weighting are unavailable. Source explicitly calculates decode from Ollama eval_count/eval_duration, and PP from prompt_eval_count/prompt_eval_duration; it excludes client/network and model-loading overhead. PP is not prompt length divided by TTFT, and 4096 is the configured context, not measured input length. The individual native counters are not published, so these averages cannot be independently recalculated. Raw harness/build commit is also unknown. Reported loaded memory for this model is 9.55 GB including KV cache at configured 4096. This is a source-reported usage figure, not installed capacity or dedicated VRAM. Unified memory is 32 GB. Cache warmness/reuse, cached-token counts, batch size/concurrency, warmup, repetitions beyond the three speed runs, FA/KV precision, speculation/MTP, thinking mode and sampling parameters for the speed test are unspecified. Do not infer them from current Ollama defaults. The article's separate 200-question accuracy tests and 84 coding problems use other generation settings. Their average output lengths, thinking switches, 8192/6912 and 2048/256 contexts/budgets are not this speed benchmark's counts or configuration. The independent SymbolStar E4B/12B results use different Unsloth files, tasks, context settings and method; do not merge the campaigns. These measurements do not establish larger-context behavior, multi-user throughput or independently validated model quality.