Model benchmarks
ornith-1.5-9b@q6_k local LLM performance
As of September 2026, ornith-1.5-9b@q6_k runs at up to 50.5 tok/s for local inference (best of 3 community benchmark runs across 2 GPUs).
Model size
9B
Peak speed
50.5 tok/s
Average speed
43.1 tok/s
Avg PP
942.3 tok/s
Min memory
4.4 GB
Max context
262.144 tokens
Avg output / run
24.678 tokens
Avg runtime / run
9m 46s
Avg quality
66.6
Benchmark runs
3
GPUs tested
2
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 3 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 | 63.0 | 66.6 | 72.7 |
| Agent Workflow | 23.9 | 43.6 | 70.0 |
| Code Generation | 47.5 | 54.3 | 60.4 |
| Role Play & Narrative | 76.2 | 82.7 | 88.4 |
| Research & Analysis | 84.2 | 86.0 | 88.1 |
Performance by hardware and tool
Every hardware/tool/quantization combination ornith-1.5-9b@q6_k 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 | Q6_K | 50.5 tok/s | 50.5 tok/s | 4.4 GB | 262.144 tokens | 63.0 | 1 |
| AMD Radeon RX 7600 XT | LM Studio | Q6_K | 39.8 tok/s | 39.5 tok/s | 7.9 GB | 65.536 tokens | 68.5 | 2 |
Benchmark runs
All 3 ornith-1.5-9b@q6_k runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is ornith-1.5-9b@q6_k good for coding?
- In our benchmarks, ornith-1.5-9b@q6_k scores 54.3/100 for coding. It runs at about 43.1 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@q6_k good for agentic (tool-using) tasks?
- In our benchmarks, ornith-1.5-9b@q6_k scores 43.6/100 for agentic workflows. It runs at about 43.1 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 ornith-1.5-9b@q6_k for local inference?
- Across 3 community benchmark runs, ornith-1.5-9b@q6_k reaches up to 50.5 tok/s and averages 43.1 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does ornith-1.5-9b@q6_k need?
- The leanest observed configuration used about 4.4 GB of memory (quantizations tested: Q6_K).
- Which tools have been used to run ornith-1.5-9b@q6_k?
- Benchmarks were submitted using LM Studio, llama.cpp. Results are community-contributed and updated as new runs arrive.