Model benchmarks
Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0 local LLM performance
As of September 2026, Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0 runs at up to 61.3 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
61.3 tok/s
Average speed
60.6 tok/s
Avg PP
548.9 tok/s
Min memory
26.4 GB
Max context
262.144 tokens
Avg output / run
79.828 tokens
Avg runtime / run
23m 6s
Avg quality
83.7
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 | 82.1 | 83.7 | 85.3 |
| Agent Workflow | 80.5 | 81.4 | 82.3 |
| Code Generation | 76.8 | 78.4 | 80.0 |
| Role Play & Narrative | 86.8 | 91.1 | 95.3 |
| Research & Analysis | 83.4 | 83.9 | 84.3 |
Performance by hardware and tool
Every hardware/tool/quantization combination Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_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 |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon Pro W7900 | llama.cpp | Q2_0 | 61.3 tok/s | 60.6 tok/s | 26.4 GB | 262.144 tokens | 83.7 | 2 |
Benchmark runs
All 2 Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_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-MTP-Q8_0 good for coding?
- In our benchmarks, Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0 scores 78.4/100 for coding. It runs at about 60.6 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-MTP-Q8_0 good for agentic (tool-using) tasks?
- In our benchmarks, Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0 scores 81.4/100 for agentic workflows. It runs at about 60.6 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-MTP-Q8_0 for local inference?
- Across 2 community benchmark runs, Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0 reaches up to 61.3 tok/s and averages 60.6 tok/s, with the fastest results on AMD Radeon Pro W7900.
- How much memory does Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0 need?
- The leanest observed configuration used about 26.4 GB of memory (quantizations tested: Q2_0).
- Which tools have been used to run Ternary-Bonsai-2-27B-PQ2_0-MTP-Q8_0?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.