Model benchmarks
ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 local LLM performance
As of October 2026, ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 runs at up to 79.9 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
79.9 tok/s
Average speed
69.2 tok/s
Avg PP
832.6 tok/s
Min memory
11.6 GB
Max context
32,768 tokens
Avg output / run
46,635 tokens
Avg runtime / run
10m 58s
Avg quality
70.7
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 | 61.8 | 70.7 | 79.5 |
| Agent Workflow | 77.8 | 79.9 | 81.9 |
| Code Generation | 3.3 | 32.7 | 62.1 |
| Role Play & Narrative | 81.0 | 83.3 | 85.6 |
| Research & Analysis | 85.3 | 86.8 | 88.3 |
Performance by hardware and tool
Every hardware/tool/quantization combination ornith-ai/Ornith-1.5-9B-GGUF:Q8_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 | Q8_0 | 79.9 tok/s | 69.2 tok/s | 11.6 GB | 32,768 tokens | 70.7 | 2 |
Benchmark runs
All 2 ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 good for coding?
- In our benchmarks, ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 scores 32.7/100 for coding. It runs at about 69.2 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~85.0 tok/s) and still scores well for coding (82.3/100).
- Is ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 good for agentic (tool-using) tasks?
- In our benchmarks, ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 scores 79.9/100 for agentic workflows. It runs at about 69.2 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:Q8_0 for local inference?
- Across 2 community benchmark runs, ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 reaches up to 79.9 tok/s and averages 69.2 tok/s, with the fastest results on AMD Radeon RX 9070/9070 XT/9070 GRE.
- How much memory does ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 need?
- The leanest observed configuration used about 11.6 GB of memory (quantizations tested: Q8_0).
- Which tools have been used to run ornith-ai/Ornith-1.5-9B-GGUF:Q8_0?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.