Model benchmarks
protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 local LLM performance
As of October 2026, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 runs at up to 61.4 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
61.4 tok/s
Average speed
58.0 tok/s
Avg PP
818.7 tok/s
Min memory
11.5 GB
Max context
32,768 tokens
Avg output / run
51,150 tokens
Avg runtime / run
14m 53s
Avg quality
63.5
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 | 63.4 | 63.5 | 63.6 |
| Agent Workflow | 76.4 | 79.7 | 83.0 |
| Code Generation | 0.3 | 2.8 | 5.2 |
| Role Play & Narrative | 82.8 | 83.2 | 83.7 |
| Research & Analysis | 87.6 | 88.4 | 89.1 |
Performance by hardware and tool
Every hardware/tool/quantization combination protoLabsAI/Ornith-1.5-9B-MTP-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 | 61.4 tok/s | 58.0 tok/s | 11.5 GB | 32,768 tokens | 63.5 | 2 |
Benchmark runs
All 2 protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 good for coding?
- In our benchmarks, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 scores 2.8/100 for coding. It runs at about 58.0 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 protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 good for agentic (tool-using) tasks?
- In our benchmarks, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 scores 79.7/100 for agentic workflows. It runs at about 58.0 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 protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 for local inference?
- Across 2 community benchmark runs, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 reaches up to 61.4 tok/s and averages 58.0 tok/s, with the fastest results on AMD Radeon RX 9070/9070 XT/9070 GRE.
- How much memory does protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0 need?
- The leanest observed configuration used about 11.5 GB of memory (quantizations tested: Q8_0).
- Which tools have been used to run protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q8_0?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.