Gemma 4 E2B-it Q4_K_M; KodeLab

Primary source: https://klab.tw/2026/06/gemma4-benchmark/ , KodeLab author Kyle, section headed discrete GPU RTX 5070 Ti (16GB). Page lists June 7, 2026 publication and August 13, 2026 update. The exact NVIDIA measurement date is unknown. These are historical source-reported measurements recovered October 13, not new runs or local reproduction. Headline: 226.3 tokens/s, the article's reported decode speed for Gemma 4 E2B-it on one NVIDIA RTX 5070 Ti with 16 GB GDDR7, Windows 11, configured num_ctx=4096. Actual prompt/output token counts, per-run values, repetition count and aggregation procedure for the NVIDIA rows are not published. Do not transfer the Mac section's explicit three-run averages to this NVIDIA row as an established fact. Model: historical Ollama library gemma4:e2b, GGUF Q4_K_M, pulled from the registry according to the article's methodology. Runtime is the article's Ollama-based setup; the exact NVIDIA Ollama version, bundled llama.cpp revision, CUDA/driver and harness build are unknown. The explicit Ollama 0.30.5 version in the article is adjacent to the Mac table and is not independently bound to the NVIDIA campaign. Verify the model tag identity at https://ollama.com/library/gemma4:e2b . Its upstream model is https://huggingface.co/google/gemma-4-E2B-it . This HF link is upstream identity only; the exact tested GGUF's HF equivalent, Ollama manifest/blob digest, weight hash, model revision and quantizer remain unknown. Today's tags and defaults are not the historical tested configuration. The article reports prefill 3461 tokens/s for this NVIDIA row. Its earlier methodology defines native rates as Ollama prompt_eval_count/prompt_eval_duration and eval_count/eval_duration and excludes network/loading costs. However, the NVIDIA section does not separately expose the harness or native counters or explicitly bind the earlier timing method to its rerun. The generic PP field is therefore left blank; retain 3461 tokens/s here as source-reported PP only. It must not be reconstructed from context, TTFT, memory or wall time. The headline is source-reported decode, not independently recomputed phase speed or end-to-end throughput. The article does not quantify this model's GPU layer split or host offload. Do not copy the earlier Mac's 100% offload statement to the NVIDIA machine. Host CPU, installed system RAM and PCIe link are unknown. Reported peak GPU memory: 3.6 GB, with no monitoring tool or sampling method supplied. This is a source-reported allocation/usage figure, separate from the card's 16 GB capacity. System RAM is unknown. GPU board variant, power limit, clock, temperatures, CPU, motherboard and storage are also unknown. Configured context is 4096, not an actual input count. The source's fixed Chinese article-generation workload and the Mac methodology provide useful campaign background, but exact NVIDIA prompt text, output cap, sampling, thinking mode, warmup, cache state/reuse, batch/concurrency, FA, KV precision and speculative decoding/MTP are not separately documented. All remain unknown. Separate accuracy/coding-evaluation counts and settings are not the speed benchmark's actual counts or denominator. The article discusses possible long-context slowdown from host offload as an inference. It explicitly does not measure longer-context NVIDIA behavior, so no extrapolated throughput or context ceiling is recorded. This row also must not be merged with the Mac mini results or the independent SymbolStar campaign.