Model benchmarks
Ornith-1.5-9B-Q8_0 local LLM performance
As of September 2026, Ornith-1.5-9B-Q8_0 runs at up to 71.9 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
71.9 tok/s
Average speed
67.7 tok/s
Avg prefill
1249.6 tok/s
Min memory
8.8 GB
Max context
131,072 tokens
Avg output / run
79,651 tokens
Avg runtime / run
19m
Avg quality
68.7
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 | 61.9 | 68.7 | 75.6 |
| Agent Workflow | 10.9 | 34.7 | 58.5 |
| Code Generation | 66.1 | 68.3 | 70.6 |
| Role Play & Narrative | 86.2 | 88.1 | 90.0 |
| Research & Analysis | 83.2 | 83.9 | 84.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination Ornith-1.5-9B-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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5060 | llama.cpp | Q8_0 | 71.9 tok/s | 67.7 tok/s | 8.8 GB | 131,072 tokens | 68.7 | 2 |
Frequently asked questions
- Is Ornith-1.5-9B-Q8_0 good for coding?
- In our benchmarks, Ornith-1.5-9B-Q8_0 scores 68.3/100 for coding. It runs at about 67.7 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 Ornith-1.5-9B-Q8_0 good for agentic (tool-using) tasks?
- In our benchmarks, Ornith-1.5-9B-Q8_0 scores 34.7/100 for agentic workflows. It runs at about 67.7 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-Q4_K_S is faster (~165.0 tok/s) and still scores well for agentic workflows (89.3/100).
- How fast is Ornith-1.5-9B-Q8_0 for local inference?
- Across 2 community benchmark runs, Ornith-1.5-9B-Q8_0 reaches up to 71.9 tok/s and averages 67.7 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Ornith-1.5-9B-Q8_0 need?
- The leanest observed configuration used about 8.8 GB of memory (quantizations tested: Q8_0).
- Which tools have been used to run Ornith-1.5-9B-Q8_0?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.