Model benchmarks
Ternary-Bonsai-2-27B-PQ2_0 local LLM performance
As of September 2026, Ternary-Bonsai-2-27B-PQ2_0 runs at up to 41.8 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
41.8 tok/s
Average speed
41.6 tok/s
Avg PP
593.5 tok/s
Min memory
13.2 GB
Max context
131.072 tokens
Avg output / run
21.146 tokens
Avg runtime / run
8m 43s
Avg quality
61.2
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 | 45.6 | 61.2 | 76.8 |
| Agent Workflow | 77.5 | 77.9 | 78.2 |
| Code Generation | 3.4 | 33.9 | 64.4 |
| Role Play & Narrative | 67.7 | 77.4 | 87.1 |
| Research & Analysis | 33.6 | 55.6 | 77.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination Ternary-Bonsai-2-27B-PQ2_0 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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5060 | llama.cpp | Q2_0 | 41.8 tok/s | 41.6 tok/s | 13.2 GB | 131.072 tokens | 61.2 | 2 |
Benchmark runs
All 2 Ternary-Bonsai-2-27B-PQ2_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Ternary-Bonsai-2-27B-PQ2_0 good for coding?
- In our benchmarks, Ternary-Bonsai-2-27B-PQ2_0 scores 33.9/100 for coding. It runs at about 41.6 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).
- Is Ternary-Bonsai-2-27B-PQ2_0 good for agentic (tool-using) tasks?
- In our benchmarks, Ternary-Bonsai-2-27B-PQ2_0 scores 77.9/100 for agentic workflows. It runs at about 41.6 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 Ternary-Bonsai-2-27B-PQ2_0 for local inference?
- Across 2 community benchmark runs, Ternary-Bonsai-2-27B-PQ2_0 reaches up to 41.8 tok/s and averages 41.6 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Ternary-Bonsai-2-27B-PQ2_0 need?
- The leanest observed configuration used about 13.2 GB of memory (quantizations tested: Q2_0).
- Which tools have been used to run Ternary-Bonsai-2-27B-PQ2_0?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.