Is ornith-ai/Ornith-1.5-9B-GGUF good for coding?

In our benchmarks, ornith-ai/Ornith-1.5-9B-GGUF scores 35.2/100 for coding. It runs at about 64.6 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~80.3 tok/s) and still scores well for coding (82.1/100).

Is ornith-ai/Ornith-1.5-9B-GGUF good for agentic (tool-using) tasks?

In our benchmarks, ornith-ai/Ornith-1.5-9B-GGUF scores 59.6/100 for agentic workflows. It runs at about 64.6 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 ornith-ai/Ornith-1.5-9B-GGUF for local inference?

Across 9 community benchmark runs, ornith-ai/Ornith-1.5-9B-GGUF reaches up to 87.2 tok/s and averages 64.6 tok/s, with the fastest results on NVIDIA GeForce RTX 4070.

How much memory does ornith-ai/Ornith-1.5-9B-GGUF need?

The leanest observed configuration used about 10.1 GB of memory (quantizations tested: IQ4_XS, Q4_K_M, Q6_K).

Which tools have been used to run ornith-ai/Ornith-1.5-9B-GGUF?

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

Model benchmarks

ornith-ai/Ornith-1.5-9B-GGUF local LLM performance

As of October 2026, ornith-ai/Ornith-1.5-9B-GGUF runs at up to 87.2 tok/s for local inference (best of 9 community benchmark runs across 1 GPU).

Unsloth StudioIQ4_XSQ4_K_MQ6_K

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Model size

Peak speed

87.2 tok/s

Average speed

64.6 tok/s

Avg PP

1414.7 tok/s

Min memory

10.1 GB

Max context

65,536 tokens

Avg output / run

64,777 tokens

Avg runtime / run

17m 8s

Avg quality

65.2

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 9 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	55.4	65.2	80.3
Agent Workflow	38.6	59.6	79.3
Code Generation	0.0	35.2	73.2
Role Play & Narrative	68.4	82.1	89.5
Research & Analysis	73.9	83.9	89.0

Performance by hardware and tool

Every hardware/tool/quantization combination ornith-ai/Ornith-1.5-9B-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 GeForce RTX 4070	Unsloth Studio	Q6_K	87.2 tok/s	61.8 tok/s	10.1 GB	65,536 tokens	66.1	6
NVIDIA GeForce RTX 4070	Unsloth Studio	Q4_K_M	80.5 tok/s	73.3 tok/s	10.1 GB	65,536 tokens	67.8	2
NVIDIA GeForce RTX 4070	Unsloth Studio	IQ4_XS	63.7 tok/s	63.7 tok/s	10.1 GB	65,536 tokens	54.8	1

Benchmark runs

All 9 ornith-ai/Ornith-1.5-9B-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is ornith-ai/Ornith-1.5-9B-GGUF good for coding?: In our benchmarks, ornith-ai/Ornith-1.5-9B-GGUF scores 35.2/100 for coding. It runs at about 64.6 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~80.3 tok/s) and still scores well for coding (82.1/100).
Is ornith-ai/Ornith-1.5-9B-GGUF good for agentic (tool-using) tasks?: In our benchmarks, ornith-ai/Ornith-1.5-9B-GGUF scores 59.6/100 for agentic workflows. It runs at about 64.6 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 ornith-ai/Ornith-1.5-9B-GGUF for local inference?: Across 9 community benchmark runs, ornith-ai/Ornith-1.5-9B-GGUF reaches up to 87.2 tok/s and averages 64.6 tok/s, with the fastest results on NVIDIA GeForce RTX 4070.
How much memory does ornith-ai/Ornith-1.5-9B-GGUF need?: The leanest observed configuration used about 10.1 GB of memory (quantizations tested: IQ4_XS, Q4_K_M, Q6_K).
Which tools have been used to run ornith-ai/Ornith-1.5-9B-GGUF?: Benchmarks were submitted using Unsloth Studio. Results are community-contributed and updated as new runs arrive.