Is prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 good for coding?

In our benchmarks, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 scores 51.2/100 for coding. It runs at about 42.3 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~91.4 tok/s) and still scores well for coding (81.6/100).

Is prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 good for agentic (tool-using) tasks?

In our benchmarks, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 scores 84.1/100 for agentic workflows. It runs at about 42.3 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 for local inference?

Across 8 community benchmark runs, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 reaches up to 47.8 tok/s and averages 42.3 tok/s, with the fastest results on AMD Radeon RX 9070/9070 XT/9070 GRE.

How much memory does prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 need?

The leanest observed configuration used about 9.3 GB of memory (quantizations tested: Q2_0).

Which tools have been used to run prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0?

Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.

Model benchmarks

prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 local LLM performance

As of October 2026, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 runs at up to 47.8 tok/s for local inference (best of 8 community benchmark runs across 1 GPU).

llama.cppQ2_0

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Model size

27B

Peak speed

47.8 tok/s

Average speed

42.3 tok/s

Avg PP

420.5 tok/s

Min memory

9.3 GB

Max context

32,768 tokens

Avg output / run

36,901 tokens

Avg runtime / run

15m 5s

Avg quality

76.7

Benchmark runs

GPUs tested

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 8 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	66.0	76.7	86.3
Agent Workflow	74.4	84.1	91.3
Code Generation	5.6	51.2	80.8
Role Play & Narrative	78.6	86.6	94.6
Research & Analysis	80.2	84.8	90.0

Performance by hardware and tool

Every hardware/tool/quantization combination prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 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
AMD Radeon RX 9070/9070 XT/9070 GRE	llama.cpp	Q2_0	47.8 tok/s	42.3 tok/s	9.3 GB	32,768 tokens	76.7	8

Benchmark runs

All 8 prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 good for coding?: In our benchmarks, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 scores 51.2/100 for coding. It runs at about 42.3 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~91.4 tok/s) and still scores well for coding (81.6/100).
Is prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 good for agentic (tool-using) tasks?: In our benchmarks, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 scores 84.1/100 for agentic workflows. It runs at about 42.3 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 for local inference?: Across 8 community benchmark runs, prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 reaches up to 47.8 tok/s and averages 42.3 tok/s, with the fastest results on AMD Radeon RX 9070/9070 XT/9070 GRE.
How much memory does prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0 need?: The leanest observed configuration used about 9.3 GB of memory (quantizations tested: Q2_0).
Which tools have been used to run prism-ml/Ternary-Bonsai-2-27B-gguf:PQ2_0?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.