Model benchmarks
ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S local LLM performance
As of October 2026, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S runs at up to 75.5 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
75.5 tok/s
Average speed
72.4 tok/s
Avg PP
545.6 tok/s
Min memory
n/a
Max context
65,536 tokens
Avg output / run
35,382 tokens
Avg runtime / run
7m 51s
Avg quality
85.8
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 | 85.3 | 85.8 | 86.7 |
| Agent Workflow | 80.7 | 81.9 | 83.5 |
| Code Generation | 81.3 | 82.3 | 83.6 |
| Role Play & Narrative | 89.4 | 90.4 | 92.2 |
| Research & Analysis | 87.4 | 88.7 | 90.1 |
Performance by hardware and tool
Every hardware/tool/quantization combination ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S 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 | llama.cpp | Q4_K_S | 75.5 tok/s | 72.4 tok/s | n/a | 65,536 tokens | 85.8 | 3 |
Benchmark runs
All 3 ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S 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:Swift-1.5-Qwen3.8-27B-Q4_K_S good for coding?
- In our benchmarks, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S scores 82.3/100 for coding. It runs at about 72.4 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~90.8 tok/s) and still scores well for coding (83.8/100).
- Is ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S good for agentic (tool-using) tasks?
- In our benchmarks, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S scores 81.9/100 for agentic workflows. It runs at about 72.4 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 ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S for local inference?
- Across 3 community benchmark runs, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S reaches up to 75.5 tok/s and averages 72.4 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
- Which tools have been used to run ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-Q4_K_S?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.