Model benchmarks
Qwen3.8-27B-Omnimerge-v6-MTP-GGUF local LLM performance
As of October 2026, Qwen3.8-27B-Omnimerge-v6-MTP-GGUF runs at up to 56.6 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
56.6 tok/s
Average speed
50.7 tok/s
Avg PP
470.9 tok/s
Min memory
30.0 GB
Max context
262,144 tokens
Avg output / run
61,874 tokens
Avg runtime / run
19m 40s
Avg quality
84.9
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 | 83.2 | 84.9 | 86.6 |
| Agent Workflow | 82.6 | 86.3 | 90.0 |
| Code Generation | 79.0 | 80.4 | 81.8 |
| Role Play & Narrative | 84.6 | 86.9 | 89.1 |
| Research & Analysis | 83.7 | 85.9 | 88.2 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-Omnimerge-v6-MTP-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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA RTX A6000 | llama.cpp | — | 56.6 tok/s | 50.7 tok/s | 30.0 GB | 262,144 tokens | 84.9 | 2 |
Benchmark runs
All 2 Qwen3.8-27B-Omnimerge-v6-MTP-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-27B-Omnimerge-v6-MTP-GGUF good for coding?
- In our benchmarks, Qwen3.8-27B-Omnimerge-v6-MTP-GGUF scores 80.4/100 for coding. It runs at about 50.7 tok/s, so if you want more speed, local-qwen is faster (~60.2 tok/s) and still scores well for coding (84.8/100).
- Is Qwen3.8-27B-Omnimerge-v6-MTP-GGUF good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-Omnimerge-v6-MTP-GGUF scores 86.3/100 for agentic workflows. It runs at about 50.7 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 Qwen3.8-27B-Omnimerge-v6-MTP-GGUF for local inference?
- Across 2 community benchmark runs, Qwen3.8-27B-Omnimerge-v6-MTP-GGUF reaches up to 56.6 tok/s and averages 50.7 tok/s, with the fastest results on NVIDIA RTX A6000.
- How much memory does Qwen3.8-27B-Omnimerge-v6-MTP-GGUF need?
- The leanest observed configuration used about 30.0 GB of memory.
- Which tools have been used to run Qwen3.8-27B-Omnimerge-v6-MTP-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.