Is google/gemma-4-12b-qat good for coding?

In our benchmarks, google/gemma-4-12b-qat scores 57.9/100 for coding. It runs at about 48.4 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).

Is google/gemma-4-12b-qat good for agentic (tool-using) tasks?

In our benchmarks, google/gemma-4-12b-qat scores 74.6/100 for agentic workflows. It runs at about 48.4 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is google/gemma-4-12b-qat for local inference?

Across 7 community benchmark runs, google/gemma-4-12b-qat reaches up to 65.9 tok/s and averages 48.4 tok/s, with the fastest results on NVIDIA GeForce RTX 4070 Ti SUPER.

How much memory does google/gemma-4-12b-qat need?

The leanest observed configuration used about 10.9 GB of memory (quantizations tested: Q4_0).

Which tools have been used to run google/gemma-4-12b-qat?

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

Model benchmarks

google/gemma-4-12b-qat local LLM performance

As of October 2026, google/gemma-4-12b-qat runs at up to 65.9 tok/s for local inference (best of 7 community benchmark runs across 2 GPUs).

LM StudioQ4_0

ShareReddit X

Model size

12B

Peak speed

65.9 tok/s

Average speed

48.4 tok/s

Avg PP

552.9 tok/s

Min memory

10.9 GB

Max context

96,000 tokens

Avg output / run

10,615 tokens

Avg runtime / run

5m 27s

Avg quality

69.8

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	63.6	69.8	75.3
Agent Workflow	67.3	74.6	81.8
Code Generation	36.9	57.9	67.3
Role Play & Narrative	67.4	78.9	87.0
Research & Analysis	49.7	68.0	77.9

Performance by hardware and tool

Every hardware/tool/quantization combination google/gemma-4-12b-qat 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 4070 Ti SUPER	LM Studio	—	65.9 tok/s	64.2 tok/s	n/a	16,384 tokens	72.4	2
CPU only	LM Studio	Q4_0	48.9 tok/s	48.2 tok/s	12.0 GB	65,536 tokens	71.2	2
AMD Radeon RX 6800/6800 XT / 6900 XT	LM Studio	Q4_0	44.6 tok/s	44.2 tok/s	10.9 GB	96,000 tokens	66.8	2
CPU only	LM Studio	—	25.9 tok/s	25.9 tok/s	n/a	8,192 tokens	68.1	1

Benchmark runs

All 7 google/gemma-4-12b-qat runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is google/gemma-4-12b-qat good for coding?: In our benchmarks, google/gemma-4-12b-qat scores 57.9/100 for coding. It runs at about 48.4 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).
Is google/gemma-4-12b-qat good for agentic (tool-using) tasks?: In our benchmarks, google/gemma-4-12b-qat scores 74.6/100 for agentic workflows. It runs at about 48.4 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is google/gemma-4-12b-qat for local inference?: Across 7 community benchmark runs, google/gemma-4-12b-qat reaches up to 65.9 tok/s and averages 48.4 tok/s, with the fastest results on NVIDIA GeForce RTX 4070 Ti SUPER.
How much memory does google/gemma-4-12b-qat need?: The leanest observed configuration used about 10.9 GB of memory (quantizations tested: Q4_0).
Which tools have been used to run google/gemma-4-12b-qat?: Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.