Model benchmarks
ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS local LLM performance
As of September 2026, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS runs at up to 71.9 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
71.9 tok/s
Average speed
71.5 tok/s
Avg PP
603.2 tok/s
Min memory
13.2 GB
Max context
65.536 tokens
Avg output / run
45.035 tokens
Avg runtime / run
9m 51s
Avg quality
83.5
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.
| Task | P5 (low) | Avg | P95 (high) |
|---|---|---|---|
| Overall | 79.7 | 83.5 | 87.4 |
| Agent Workflow | 82.4 | 85.9 | 89.4 |
| Code Generation | 68.0 | 74.0 | 79.9 |
| Role Play & Narrative | 88.6 | 90.6 | 92.6 |
| Research & Analysis | 79.7 | 83.7 | 87.8 |
Performance by hardware and tool
Every hardware/tool/quantization combination ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M | llama.cpp | — | 71.9 tok/s | 71.5 tok/s | 13.2 GB | 65.536 tokens | 83.5 | 2 |
Benchmark runs
All 2 ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS 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-IQ4_XS good for coding?
- In our benchmarks, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS scores 74.0/100 for coding. It runs at about 71.5 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 ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS good for agentic (tool-using) tasks?
- In our benchmarks, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS scores 85.9/100 for agentic workflows. It runs at about 71.5 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS is faster (~103.5 tok/s) and still scores well for agentic workflows (88.3/100).
- How fast is ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS for local inference?
- Across 2 community benchmark runs, ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS reaches up to 71.9 tok/s and averages 71.5 tok/s, with the fastest results on AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M.
- How much memory does ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS need?
- The leanest observed configuration used about 13.2 GB of memory.
- Which tools have been used to run ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Swift-1.5-Qwen3.8-27B-IQ4_XS?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.