Model benchmarks
google/gemma-4-12b local LLM performance
As of September 2026, google/gemma-4-12b runs at up to 32.4 tok/s for local inference (best of 3 community benchmark runs across 2 GPUs).
Model size
12B
Peak speed
32.4 tok/s
Average speed
26.3 tok/s
Avg PP
220.3 tok/s
Min memory
7.0 GB
Max context
16.384 tokens
Avg output / run
11.897 tokens
Avg runtime / run
10m 6s
Avg quality
73.3
Benchmark runs
3
GPUs tested
2
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 | 73.1 | 73.3 | 73.4 |
| Agent Workflow | 73.9 | 80.0 | 83.7 |
| Code Generation | 63.3 | 65.2 | 66.4 |
| Role Play & Narrative | 72.7 | 78.5 | 85.8 |
| Research & Analysis | 60.3 | 69.4 | 77.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination google/gemma-4-12b 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 |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 6800 XT | LM Studio | — | 32.4 tok/s | 32.4 tok/s | 9.3 GB | 8.192 tokens | 73.4 | 1 |
| NVIDIA GeForce RTX 4070 Ti SUPER | LM Studio | — | 24.9 tok/s | 24.9 tok/s | 12.0 GB | 16.384 tokens | 73.3 | 1 |
| CPU only | LM Studio | — | 21.6 tok/s | 21.6 tok/s | 7.0 GB | 8.192 tokens | 73.1 | 1 |
Benchmark runs
All 3 google/gemma-4-12b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is google/gemma-4-12b good for coding?
- In our benchmarks, google/gemma-4-12b scores 65.2/100 for coding. It runs at about 26.3 tok/s, so if you want more speed, /home/polaris/models/Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL.gguf is faster (~35.6 tok/s) and still scores well for coding (84.9/100).
- Is google/gemma-4-12b good for agentic (tool-using) tasks?
- In our benchmarks, google/gemma-4-12b scores 80.0/100 for agentic workflows. It runs at about 26.3 tok/s, so if you want more speed, muse-glimmer:latest is faster (~34.5 tok/s) and still scores well for agentic workflows (93.0/100).
- How fast is google/gemma-4-12b for local inference?
- Across 3 community benchmark runs, google/gemma-4-12b reaches up to 32.4 tok/s and averages 26.3 tok/s, with the fastest results on AMD Radeon RX 6800 XT.
- How much memory does google/gemma-4-12b need?
- The leanest observed configuration used about 7.0 GB of memory.
- Which tools have been used to run google/gemma-4-12b?
- Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.