Model benchmarks
google/gemma-4-31b local LLM performance
As of September 2026, google/gemma-4-31b runs at up to 20.0 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
Model size
31B
Peak speed
20.0 tok/s
Average speed
17.1 tok/s
Avg PP
410.0 tok/s
Min memory
n/a
Max context
65,536 tokens
Avg output / run
10,292 tokens
Avg runtime / run
9m 42s
Avg quality
53.4
Benchmark runs
2
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 2 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 | 36.7 | 53.4 | 70.2 |
| Agent Workflow | 67.6 | 76.0 | 84.5 |
| Code Generation | 2.9 | 28.6 | 54.2 |
| Role Play & Narrative | 72.8 | 75.3 | 77.7 |
| Research & Analysis | 3.4 | 33.8 | 64.2 |
Performance by hardware and tool
Every hardware/tool/quantization combination google/gemma-4-31b 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 |
|---|---|---|---|---|---|---|---|---|
| Apple M5 Max | LM Studio | — | 20.0 tok/s | 20.0 tok/s | n/a | 65,536 tokens | 72.0 | 1 |
| AMD Radeon RX 7900 XTX | LM Studio | — | 14.2 tok/s | 14.2 tok/s | n/a | 65,536 tokens | 34.8 | 1 |
Benchmark runs
All 2 google/gemma-4-31b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is google/gemma-4-31b good for coding?
- In our benchmarks, google/gemma-4-31b scores 28.6/100 for coding. It runs at about 17.1 tok/s, so if you want more speed, IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF:1 is faster (~25.7 tok/s) and still scores well for coding (85.0/100).
- Is google/gemma-4-31b good for agentic (tool-using) tasks?
- In our benchmarks, google/gemma-4-31b scores 76.0/100 for agentic workflows. It runs at about 17.1 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-4-31b for local inference?
- Across 2 community benchmark runs, google/gemma-4-31b reaches up to 20.0 tok/s and averages 17.1 tok/s, with the fastest results on Apple M5 Max.
- Which tools have been used to run google/gemma-4-31b?
- Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.