Model benchmarks

Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL local LLM performance

As of September 2026, Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL runs at up to 39.8 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).

llama.cppQ4_K
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Model size

35B

Peak speed

39.8 tok/s

Average speed

39.4 tok/s

Avg PP

217.4 tok/s

Min memory

17.1 GB

Max context

86.016 tokens

Avg output / run

42.435 tokens

Avg runtime / run

18m 12s

Avg quality

86.4

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.

TaskP5 (low)AvgP95 (high)
Overall86.286.486.6
Agent Workflow92.192.893.4
Code Generation78.678.879.0
Role Play & Narrative86.287.689.1
Research & Analysis86.386.486.5

Performance by hardware and tool

Every hardware/tool/quantization combination Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
NVIDIA GeForce RTX 3080 Tillama.cppQ4_K39.8 tok/s39.4 tok/s17.1 GB86.016 tokens86.42

Benchmark runs

All 2 Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Frequently asked questions

Is Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL good for coding?
In our benchmarks, Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL scores 78.8/100 for coding. It runs at about 39.4 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 Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL good for agentic (tool-using) tasks?
In our benchmarks, Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL scores 92.8/100 for agentic workflows, among the top 3 for agentic workflows on consumer hardware (≤24 GB VRAM). It runs at about 39.4 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 Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL for local inference?
Across 2 community benchmark runs, Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL reaches up to 39.8 tok/s and averages 39.4 tok/s, with the fastest results on NVIDIA GeForce RTX 3080 Ti.
How much memory does Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL need?
The leanest observed configuration used about 17.1 GB of memory (quantizations tested: Q4_K).
Which tools have been used to run Unsloth-Ornith-1.5-35B-A3B-UD-Q4_K_XL?
Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.