Model benchmarks
bartowski/Ornith-1.5-9B-GGUF:IQ4_XS local LLM performance
As of October 2026, bartowski/Ornith-1.5-9B-GGUF:IQ4_XS runs at up to 82.2 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
82.2 tok/s
Average speed
70.3 tok/s
Avg PP
809.5 tok/s
Min memory
11.3 GB
Max context
65,536 tokens
Avg output / run
27,544 tokens
Avg runtime / run
6m 25s
Avg quality
61.1
Benchmark runs
3
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 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 | 58.0 | 61.1 | 64.3 |
| Agent Workflow | 17.8 | 32.7 | 54.4 |
| Code Generation | 35.2 | 52.0 | 72.5 |
| Role Play & Narrative | 81.4 | 85.4 | 90.8 |
| Research & Analysis | 51.4 | 74.4 | 90.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination bartowski/Ornith-1.5-9B-GGUF:IQ4_XS 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 | llama.cpp | — | 82.2 tok/s | 70.3 tok/s | 11.3 GB | 65,536 tokens | 61.1 | 3 |
Benchmark runs
All 3 bartowski/Ornith-1.5-9B-GGUF:IQ4_XS runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is bartowski/Ornith-1.5-9B-GGUF:IQ4_XS good for coding?
- In our benchmarks, bartowski/Ornith-1.5-9B-GGUF:IQ4_XS scores 52.0/100 for coding. It runs at about 70.3 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 bartowski/Ornith-1.5-9B-GGUF:IQ4_XS good for agentic (tool-using) tasks?
- In our benchmarks, bartowski/Ornith-1.5-9B-GGUF:IQ4_XS scores 32.7/100 for agentic workflows. It runs at about 70.3 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 bartowski/Ornith-1.5-9B-GGUF:IQ4_XS for local inference?
- Across 3 community benchmark runs, bartowski/Ornith-1.5-9B-GGUF:IQ4_XS reaches up to 82.2 tok/s and averages 70.3 tok/s, with the fastest results on NVIDIA GeForce RTX 4070.
- How much memory does bartowski/Ornith-1.5-9B-GGUF:IQ4_XS need?
- The leanest observed configuration used about 11.3 GB of memory.
- Which tools have been used to run bartowski/Ornith-1.5-9B-GGUF:IQ4_XS?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.