Is gemma-4-E4B-it-Q4_0 good for coding?

In our benchmarks, gemma-4-E4B-it-Q4_0 scores 56.3/100 for coding. It runs at about 83.5 tok/s, so if you want more speed, Ornith-1.5-35B-A3B.APEX-I-NanoPlus is faster (~110.5 tok/s) and still scores well for coding (81.1/100).

Is gemma-4-E4B-it-Q4_0 good for agentic (tool-using) tasks?

In our benchmarks, gemma-4-E4B-it-Q4_0 scores 71.6/100 for agentic workflows. It runs at about 83.5 tok/s, so if you want more speed, qwen3.8-flash-next-iq3_s is faster (~104.4 tok/s) and still scores well for agentic workflows (91.2/100).

How fast is gemma-4-E4B-it-Q4_0 for local inference?

Across 7 community benchmark runs, gemma-4-E4B-it-Q4_0 reaches up to 99.9 tok/s and averages 83.5 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.

How much memory does gemma-4-E4B-it-Q4_0 need?

The leanest observed configuration used about 4.1 GB of memory (quantizations tested: Q4_0).

Which tools have been used to run gemma-4-E4B-it-Q4_0?

Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.

Model benchmarks

gemma-4-E4B-it-Q4_0 local LLM performance

As of October 2026, gemma-4-E4B-it-Q4_0 runs at up to 99.9 tok/s for local inference (best of 7 community benchmark runs across 1 GPU).

llama.cppQ4_0

ShareReddit X

Model size

Peak speed

99.9 tok/s

Average speed

83.5 tok/s

Avg PP

871.1 tok/s

Min memory

4.1 GB

Max context

32,768 tokens

Avg output / run

9,459 tokens

Avg runtime / run

1m 55s

Avg quality

66.6

Benchmark runs

GPUs tested

Quality by task

Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 7 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

Task	P5 (low)	Avg	P95 (high)
Overall	58.5	66.6	72.0
Agent Workflow	49.8	71.6	85.1
Code Generation	40.7	56.3	67.5
Role Play & Narrative	66.2	73.3	79.9
Research & Analysis	61.4	65.3	68.7

Performance by hardware and tool

Every hardware/tool/quantization combination gemma-4-E4B-it-Q4_0 has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
NVIDIA GeForce RTX 4060 Laptop GPU	llama.cpp	Q4_0	99.9 tok/s	83.5 tok/s	4.1 GB	32,768 tokens	66.6	7

Benchmark runs

All 7 gemma-4-E4B-it-Q4_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is gemma-4-E4B-it-Q4_0 good for coding?: In our benchmarks, gemma-4-E4B-it-Q4_0 scores 56.3/100 for coding. It runs at about 83.5 tok/s, so if you want more speed, Ornith-1.5-35B-A3B.APEX-I-NanoPlus is faster (~110.5 tok/s) and still scores well for coding (81.1/100).
Is gemma-4-E4B-it-Q4_0 good for agentic (tool-using) tasks?: In our benchmarks, gemma-4-E4B-it-Q4_0 scores 71.6/100 for agentic workflows. It runs at about 83.5 tok/s, so if you want more speed, qwen3.8-flash-next-iq3_s is faster (~104.4 tok/s) and still scores well for agentic workflows (91.2/100).
How fast is gemma-4-E4B-it-Q4_0 for local inference?: Across 7 community benchmark runs, gemma-4-E4B-it-Q4_0 reaches up to 99.9 tok/s and averages 83.5 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.
How much memory does gemma-4-E4B-it-Q4_0 need?: The leanest observed configuration used about 4.1 GB of memory (quantizations tested: Q4_0).
Which tools have been used to run gemma-4-E4B-it-Q4_0?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.