Model benchmarks
protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M local LLM performance
As of October 2026, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M runs at up to 92.7 tok/s for local inference (best of 5 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
92.7 tok/s
Average speed
78.9 tok/s
Avg PP
892.0 tok/s
Min memory
7.9 GB
Max context
32,768 tokens
Avg output / run
29,760 tokens
Avg runtime / run
6m 15s
Avg quality
74.5
Benchmark runs
5
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 5 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 | 65.4 | 74.5 | 78.9 |
| Agent Workflow | 65.4 | 76.4 | 82.6 |
| Code Generation | 15.1 | 53.8 | 76.4 |
| Role Play & Narrative | 67.5 | 82.5 | 90.5 |
| Research & Analysis | 82.7 | 85.5 | 87.9 |
Performance by hardware and tool
Every hardware/tool/quantization combination protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M 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 | Q5_K_M | 92.7 tok/s | 78.9 tok/s | 7.9 GB | 32,768 tokens | 74.5 | 5 |
Benchmark runs
All 5 protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M 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:Q5_K_M good for coding?
- In our benchmarks, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M scores 53.8/100 for coding. It runs at about 78.9 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:Q5_K_M good for agentic (tool-using) tasks?
- In our benchmarks, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M scores 76.4/100 for agentic workflows. It runs at about 78.9 tok/s, so if you want more speed, qwen3.8-flash-next-iq3_s is faster (~116.8 tok/s) and still scores well for agentic workflows (88.4/100).
- How fast is protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M for local inference?
- Across 5 community benchmark runs, protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M reaches up to 92.7 tok/s and averages 78.9 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:Q5_K_M need?
- The leanest observed configuration used about 7.9 GB of memory (quantizations tested: Q5_K_M).
- Which tools have been used to run protoLabsAI/Ornith-1.5-9B-MTP-GGUF:Q5_K_M?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.