Is unsloth/Qwen3.8-27B-GGUF good for coding?

In our benchmarks, unsloth/Qwen3.8-27B-GGUF scores 70.3/100 for coding. It runs at about 46.3 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).

Is unsloth/Qwen3.8-27B-GGUF good for agentic (tool-using) tasks?

In our benchmarks, unsloth/Qwen3.8-27B-GGUF scores 83.1/100 for agentic workflows. It runs at about 46.3 tok/s, so if you want more speed, swift-iq2_xs is faster (~91.5 tok/s) and still scores well for agentic workflows (91.4/100).

How fast is unsloth/Qwen3.8-27B-GGUF for local inference?

Across 64 community benchmark runs, unsloth/Qwen3.8-27B-GGUF reaches up to 70.7 tok/s and averages 46.3 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.

How much memory does unsloth/Qwen3.8-27B-GGUF need?

The leanest observed configuration used about 11.5 GB of memory (quantizations tested: Q4_K_M, Q8_0, UD-IQ2_XXS, UD-IQ3_XXS, UD-Q4_K_M, UD-Q4_K_XL, UD-Q6_K_XL).

Which tools have been used to run unsloth/Qwen3.8-27B-GGUF?

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

Model benchmarks

unsloth/Qwen3.8-27B-GGUF local LLM performance

As of October 2026, unsloth/Qwen3.8-27B-GGUF runs at up to 70.7 tok/s for local inference (best of 64 community benchmark runs across 5 GPUs).

Unsloth Studiollama.cppQ4_K_MQ8_0UD-IQ2_XXSUD-IQ3_XXSUD-Q4_K_MUD-Q4_K_XLUD-Q6_K_XL

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Model size

27B

Peak speed

70.7 tok/s

Average speed

46.3 tok/s

Avg PP

491.0 tok/s

Min memory

11.5 GB

Max context

262,144 tokens

Avg output / run

45,326 tokens

Avg runtime / run

18m 22s

Avg quality

81.4

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 64 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	52.5	81.4	89.6
Agent Workflow	71.7	83.1	91.5
Code Generation	0.0	70.3	87.1
Role Play & Narrative	83.7	89.8	95.5
Research & Analysis	56.1	82.4	90.3

Performance by hardware and tool

Every hardware/tool/quantization combination unsloth/Qwen3.8-27B-GGUF 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 7900 XTX	Unsloth Studio	UD-Q4_K_XL	70.7 tok/s	67.9 tok/s	n/a	131,072 tokens	82.2	3
AMD Radeon RX 7900 XTX	Unsloth Studio	—	63.6 tok/s	53.2 tok/s	n/a	131,072 tokens	84.4	11
AMD Radeon RX 7900 XTX	Unsloth Studio	Q8_0	62.7 tok/s	44.3 tok/s	n/a	131,072 tokens	85.9	7
AMD Radeon RX 7900 XTX	Unsloth Studio	UD-Q6_K_XL	62.3 tok/s	62.3 tok/s	n/a	262,144 tokens	86.1	1
AMD Radeon RX 7900 XTX	Unsloth Studio	UD-Q4_K_M	57.4 tok/s	46.3 tok/s	n/a	180,000 tokens	87.1	18
AMD Radeon RX 9070 XT	Unsloth Studio	UD-IQ3_XXS	55.5 tok/s	55.5 tok/s	15.7 GB	8,192 tokens	81.7	1
AMD Radeon RX 7900 XTX	Unsloth Studio	Q4_K_M	51.7 tok/s	43.9 tok/s	n/a	65,536 tokens	86.9	10
NVIDIA GeForce RTX 4070	llama.cpp	—	42.1 tok/s	39.3 tok/s	n/a	16,384 tokens	56.0	5
NVIDIA GeForce RTX 4070	Unsloth Studio	UD-Q4_K_XL	41.7 tok/s	41.5 tok/s	n/a	16,384 tokens	50.6	3
Apple M5 Max	Unsloth Studio	UD-Q6_K_XL	40.4 tok/s	40.1 tok/s	n/a	65,536 tokens	83.7	2
Apple M5 Max	llama.cpp	—	29.5 tok/s	29.5 tok/s	18.4 GB	131,072 tokens	86.7	2
NVIDIA GeForce RTX 3080	Unsloth Studio	UD-IQ2_XXS	12.0 tok/s	12.0 tok/s	11.5 GB	100,000 tokens	58.0	1

Benchmark runs

All 64 unsloth/Qwen3.8-27B-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is unsloth/Qwen3.8-27B-GGUF good for coding?: In our benchmarks, unsloth/Qwen3.8-27B-GGUF scores 70.3/100 for coding. It runs at about 46.3 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).
Is unsloth/Qwen3.8-27B-GGUF good for agentic (tool-using) tasks?: In our benchmarks, unsloth/Qwen3.8-27B-GGUF scores 83.1/100 for agentic workflows. It runs at about 46.3 tok/s, so if you want more speed, swift-iq2_xs is faster (~91.5 tok/s) and still scores well for agentic workflows (91.4/100).
How fast is unsloth/Qwen3.8-27B-GGUF for local inference?: Across 64 community benchmark runs, unsloth/Qwen3.8-27B-GGUF reaches up to 70.7 tok/s and averages 46.3 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
How much memory does unsloth/Qwen3.8-27B-GGUF need?: The leanest observed configuration used about 11.5 GB of memory (quantizations tested: Q4_K_M, Q8_0, UD-IQ2_XXS, UD-IQ3_XXS, UD-Q4_K_M, UD-Q4_K_XL, UD-Q6_K_XL).
Which tools have been used to run unsloth/Qwen3.8-27B-GGUF?: Benchmarks were submitted using Unsloth Studio, llama.cpp. Results are community-contributed and updated as new runs arrive.