Model benchmarks

ukisai/Swift-1.5-Qwen3.8-27B-GGUF local LLM performance

As of September 2026, ukisai/Swift-1.5-Qwen3.8-27B-GGUF runs at up to 64.7 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).

Unsloth StudioQ4_K_MQ6_K
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Model size

27B

Peak speed

64.7 tok/s

Average speed

55.3 tok/s

Avg PP

186.3 tok/s

Min memory

1164.8 GB

Max context

131,072 tokens

Avg output / run

26,940 tokens

Avg runtime / run

8m 45s

Avg quality

78.1

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.

TaskP5 (low)AvgP95 (high)
Overall69.078.187.7
Agent Workflow85.687.390.0
Code Generation15.352.480.0
Role Play & Narrative77.488.095.0
Research & Analysis82.784.887.6

Performance by hardware and tool

Every hardware/tool/quantization combination ukisai/Swift-1.5-Qwen3.8-27B-GGUF has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
AMD Radeon RX 7900 XT/7900 XTX/7900MUnsloth StudioQ6_K64.7 tok/s62.7 tok/s1449.2 GB131,072 tokens83.22
AMD Radeon RX 7900 XT/7900 XTX/7900MUnsloth StudioQ4_K_M40.7 tok/s40.7 tok/s1164.8 GB8,192 tokens68.11

Benchmark runs

All 3 ukisai/Swift-1.5-Qwen3.8-27B-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Frequently asked questions

Is ukisai/Swift-1.5-Qwen3.8-27B-GGUF good for coding?
In our benchmarks, ukisai/Swift-1.5-Qwen3.8-27B-GGUF scores 52.4/100 for coding. It runs at about 55.3 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~96.3 tok/s) and still scores well for coding (83.4/100).
Is ukisai/Swift-1.5-Qwen3.8-27B-GGUF good for agentic (tool-using) tasks?
In our benchmarks, ukisai/Swift-1.5-Qwen3.8-27B-GGUF scores 87.3/100 for agentic workflows. It runs at about 55.3 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 ukisai/Swift-1.5-Qwen3.8-27B-GGUF for local inference?
Across 3 community benchmark runs, ukisai/Swift-1.5-Qwen3.8-27B-GGUF reaches up to 64.7 tok/s and averages 55.3 tok/s, with the fastest results on AMD Radeon RX 7900 XT/7900 XTX/7900M.
How much memory does ukisai/Swift-1.5-Qwen3.8-27B-GGUF need?
The leanest observed configuration used about 1164.8 GB of memory (quantizations tested: Q4_K_M, Q6_K).
Which tools have been used to run ukisai/Swift-1.5-Qwen3.8-27B-GGUF?
Benchmarks were submitted using Unsloth Studio. Results are community-contributed and updated as new runs arrive.