Model benchmarks
yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF local LLM performance
As of October 2026, yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF runs at up to 7.4 tok/s for local inference (best of 4 community benchmark runs across 1 GPU).
Model size
12B
Peak speed
7.4 tok/s
Average speed
7.4 tok/s
Avg PP
199.5 tok/s
Min memory
n/a
Max context
65,535 tokens
Avg output / run
4,599 tokens
Avg runtime / run
11m 52s
Avg quality
58.3
Benchmark runs
4
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 4 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 | 53.5 | 58.3 | 65.1 |
| Agent Workflow | 41.6 | 62.8 | 79.1 |
| Code Generation | 20.9 | 42.4 | 53.6 |
| Role Play & Narrative | 48.4 | 64.5 | 73.7 |
| Research & Analysis | 59.9 | 63.3 | 69.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF 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 |
|---|---|---|---|---|---|---|---|---|
| Intel Arc B390 | llama.cpp | — | 7.4 tok/s | 7.4 tok/s | n/a | 65,535 tokens | 58.3 | 4 |
Benchmark runs
All 4 yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF good for coding?
- In our benchmarks, yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF scores 42.4/100 for coding. It runs at about 7.4 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 yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF good for agentic (tool-using) tasks?
- In our benchmarks, yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF scores 62.8/100 for agentic workflows. It runs at about 7.4 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 yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF for local inference?
- Across 4 community benchmark runs, yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF reaches up to 7.4 tok/s and averages 7.4 tok/s, with the fastest results on Intel Arc B390.
- Which tools have been used to run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.