Model benchmarks
Ornith-1.5-35B-A3B-Q5_K_M local LLM performance
As of September 2026, Ornith-1.5-35B-A3B-Q5_K_M runs at up to 33.6 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
35B
Peak speed
33.6 tok/s
Average speed
33.4 tok/s
Avg PP
260.0 tok/s
Min memory
21.4 GB
Max context
65.536 tokens
Avg output / run
11.661 tokens
Avg runtime / run
6m 5s
Avg quality
78.4
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 | 76.6 | 78.4 | 80.0 |
| Agent Workflow | 82.7 | 82.8 | 82.9 |
| Code Generation | 71.6 | 71.9 | 72.3 |
| Role Play & Narrative | 82.2 | 83.3 | 84.3 |
| Research & Analysis | 69.7 | 75.5 | 81.2 |
Performance by hardware and tool
Every hardware/tool/quantization combination Ornith-1.5-35B-A3B-Q5_K_M 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 |
|---|---|---|---|---|---|---|---|---|
| Tesla P100-PCIE-16GB | llama.cpp | Q5_K_M | 33.6 tok/s | 33.4 tok/s | 21.4 GB | 65.536 tokens | 78.4 | 3 |
Benchmark runs
All 3 Ornith-1.5-35B-A3B-Q5_K_M runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Ornith-1.5-35B-A3B-Q5_K_M good for coding?
- In our benchmarks, Ornith-1.5-35B-A3B-Q5_K_M scores 71.9/100 for coding. It runs at about 33.4 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-1.5-35B-A3B-Q5_K_M good for agentic (tool-using) tasks?
- In our benchmarks, Ornith-1.5-35B-A3B-Q5_K_M scores 82.8/100 for agentic workflows. It runs at about 33.4 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-1.5-35B-A3B-Q5_K_M for local inference?
- Across 3 community benchmark runs, Ornith-1.5-35B-A3B-Q5_K_M reaches up to 33.6 tok/s and averages 33.4 tok/s, with the fastest results on Tesla P100-PCIE-16GB.
- How much memory does Ornith-1.5-35B-A3B-Q5_K_M need?
- The leanest observed configuration used about 21.4 GB of memory (quantizations tested: Q5_K_M).
- Which tools have been used to run Ornith-1.5-35B-A3B-Q5_K_M?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.