Is unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL good for coding?

In our benchmarks, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL scores 65.0/100 for coding. It runs at about 49.0 tok/s, so if you want more speed, ornith-1.5-35b-a3b-apex-mtp-i-quality is faster (~105.2 tok/s) and still scores well for coding (83.6/100).

Is unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL good for agentic (tool-using) tasks?

In our benchmarks, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL scores 77.3/100 for agentic workflows. It runs at about 49.0 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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL for local inference?

Across 10 community benchmark runs, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL reaches up to 54.0 tok/s and averages 49.0 tok/s, with the fastest results on AMD Radeon AI PRO R9700.

How much memory does unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL need?

The leanest observed configuration used about 21.9 GB of memory (quantizations tested: Q4_K).

Which tools have been used to run unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL?

Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.

Model benchmarks

unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL local LLM performance

As of October 2026, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL runs at up to 54.0 tok/s for local inference (best of 10 community benchmark runs across 3 GPUs).

llama.cppQ4_K

ShareReddit X

Model size

31B

Peak speed

54.0 tok/s

Average speed

49.0 tok/s

Avg PP

523.7 tok/s

Min memory

21.9 GB

Max context

131,072 tokens

Avg output / run

7,414 tokens

Avg runtime / run

2m 47s

Avg quality

71.2

Benchmark runs

GPUs tested

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 10 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	66.1	71.2	77.2
Agent Workflow	72.7	77.3	82.9
Code Generation	53.9	65.0	72.8
Role Play & Narrative	62.3	71.2	81.3
Research & Analysis	62.4	71.4	80.9

Performance by hardware and tool

Every hardware/tool/quantization combination unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL 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 AI PRO R9700	llama.cpp	Q4_K	54.0 tok/s	53.7 tok/s	21.9 GB	131,072 tokens	71.9	5
Advanced Micro Devices, Inc. [AMD/ATI] Device 7551	llama.cpp	Q4_K	52.7 tok/s	42.3 tok/s	24.1 GB	65,536 tokens	71.8	3
NVIDIA GeForce RTX 3080 Ti	llama.cpp	Q4_K	47.8 tok/s	47.1 tok/s	26.1 GB	65,536 tokens	68.7	2

Benchmark runs

All 10 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL good for coding?: In our benchmarks, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL scores 65.0/100 for coding. It runs at about 49.0 tok/s, so if you want more speed, ornith-1.5-35b-a3b-apex-mtp-i-quality is faster (~105.2 tok/s) and still scores well for coding (83.6/100).
Is unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL good for agentic (tool-using) tasks?: In our benchmarks, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL scores 77.3/100 for agentic workflows. It runs at about 49.0 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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL for local inference?: Across 10 community benchmark runs, unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL reaches up to 54.0 tok/s and averages 49.0 tok/s, with the fastest results on AMD Radeon AI PRO R9700.
How much memory does unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL need?: The leanest observed configuration used about 21.9 GB of memory (quantizations tested: Q4_K).
Which tools have been used to run unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.