Model benchmarks
JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M local LLM performance
As of October 2026, JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M runs at up to 16.4 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
12B
Peak speed
16.4 tok/s
Average speed
15.6 tok/s
Avg PP
169.6 tok/s
Min memory
n/a
Max context
65,536 tokens
Avg output / run
27,759 tokens
Avg runtime / run
31m 43s
Avg quality
63.3
Benchmark runs
2
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 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 | 62.4 | 63.3 | 64.2 |
| Agent Workflow | 55.9 | 67.6 | 79.2 |
| Code Generation | 58.2 | 59.7 | 61.2 |
| Role Play & Narrative | 56.3 | 62.1 | 67.8 |
| Research & Analysis | 55.8 | 63.9 | 72.1 |
Performance by hardware and tool
Every hardware/tool/quantization combination JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M 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 2060 | llama.cpp | Q4_K_M | 16.4 tok/s | 15.6 tok/s | n/a | 65,536 tokens | 63.3 | 2 |
Benchmark runs
All 2 JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M good for coding?
- In our benchmarks, JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M scores 59.7/100 for coding. It runs at about 15.6 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 JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M good for agentic (tool-using) tasks?
- In our benchmarks, JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M scores 67.6/100 for agentic workflows. It runs at about 15.6 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 JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M for local inference?
- Across 2 community benchmark runs, JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M reaches up to 16.4 tok/s and averages 15.6 tok/s, with the fastest results on NVIDIA GeForce RTX 2060.
- Which tools have been used to run JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF:Q4_K_M?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.