Model benchmarks
google/gemma-4-12b-qat local LLM performance
As of September 2026, google/gemma-4-12b-qat runs at up to 65.9 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
12B
Peak speed
65.9 tok/s
Average speed
51.4 tok/s
Avg PP
288.0 tok/s
Min memory
6.7 GB
Max context
16.384 tokens
Avg output / run
12.599 tokens
Avg runtime / run
8m 55s
Avg quality
71.0
Benchmark runs
3
GPUs tested
1
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 3 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 | 68.2 | 71.0 | 74.9 |
| Agent Workflow | 75.2 | 78.3 | 83.0 |
| Code Generation | 62.4 | 65.0 | 67.7 |
| Role Play & Narrative | 67.0 | 78.7 | 87.6 |
| Research & Analysis | 46.1 | 61.9 | 73.2 |
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 September 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 | 6.7 GB | 16.384 tokens | 72.4 | 2 |
| CPU only | LM Studio | — | 25.9 tok/s | 25.9 tok/s | 6.7 GB | 8.192 tokens | 68.1 | 1 |
Benchmark runs
All 3 google/gemma-4-12b-qat runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is google/gemma-4-12b-qat good for coding?
- In our benchmarks, google/gemma-4-12b-qat scores 65.0/100 for coding. It runs at about 51.4 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-4-12b-qat good for agentic (tool-using) tasks?
- In our benchmarks, google/gemma-4-12b-qat scores 78.3/100 for agentic workflows. It runs at about 51.4 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-4-12b-qat for local inference?
- Across 3 community benchmark runs, google/gemma-4-12b-qat reaches up to 65.9 tok/s and averages 51.4 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 6.7 GB of memory.
- 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.