Is google/gemma-4-e2b good for coding?

In our benchmarks, google/gemma-4-e2b scores 47.9/100 for coding. It runs at about 44.5 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).

Is google/gemma-4-e2b good for agentic (tool-using) tasks?

In our benchmarks, google/gemma-4-e2b scores 68.7/100 for agentic workflows. It runs at about 44.5 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).

How fast is google/gemma-4-e2b for local inference?

Across 5 community benchmark runs, google/gemma-4-e2b reaches up to 57.1 tok/s and averages 44.5 tok/s, with the fastest results on Intel(R) Arc(TM) Pro 140T GPU (16GB).

How much memory does google/gemma-4-e2b need?

The leanest observed configuration used about 4.1 GB of memory.

Which tools have been used to run google/gemma-4-e2b?

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

Model benchmarks

google/gemma-4-e2b local LLM performance

As of September 2026, google/gemma-4-e2b runs at up to 57.1 tok/s for local inference (best of 5 community benchmark runs across 2 GPUs).

LM Studio

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Model size

Peak speed

57.1 tok/s

Average speed

44.5 tok/s

Avg PP

903.6 tok/s

Min memory

4.1 GB

Max context

8.192 tokens

Avg output / run

8.596 tokens

Avg runtime / run

3m 53s

Avg quality

64.1

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 5 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	61.1	64.1	67.6
Agent Workflow	62.3	68.7	74.2
Code Generation	38.7	47.9	55.1
Role Play & Narrative	71.0	76.9	82.4
Research & Analysis	50.9	62.7	67.7

Performance by hardware and tool

Every hardware/tool/quantization combination google/gemma-4-e2b has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
Intel(R) Arc(TM) Pro 140T GPU (16GB)	LM Studio	—	57.1 tok/s	46.7 tok/s	4.1 GB	8.192 tokens	63.2	3
AMD Radeon RX 6600	LM Studio	—	41.9 tok/s	41.2 tok/s	4.1 GB	8.192 tokens	65.5	2

Benchmark runs

All 5 google/gemma-4-e2b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Frequently asked questions

Is google/gemma-4-e2b good for coding?: In our benchmarks, google/gemma-4-e2b scores 47.9/100 for coding. It runs at about 44.5 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).
Is google/gemma-4-e2b good for agentic (tool-using) tasks?: In our benchmarks, google/gemma-4-e2b scores 68.7/100 for agentic workflows. It runs at about 44.5 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
How fast is google/gemma-4-e2b for local inference?: Across 5 community benchmark runs, google/gemma-4-e2b reaches up to 57.1 tok/s and averages 44.5 tok/s, with the fastest results on Intel(R) Arc(TM) Pro 140T GPU (16GB).
How much memory does google/gemma-4-e2b need?: The leanest observed configuration used about 4.1 GB of memory.
Which tools have been used to run google/gemma-4-e2b?: Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.