Model benchmarks
Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp local LLM performance
As of September 2026, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp runs at up to 39.7 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
39.7 tok/s
Average speed
39.2 tok/s
Avg PP
498.3 tok/s
Min memory
16.2 GB
Max context
131,072 tokens
Avg output / run
33,746 tokens
Avg runtime / run
14m 22s
Avg quality
79.8
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 | 72.7 | 79.8 | 86.2 |
| Agent Workflow | 76.5 | 81.9 | 88.1 |
| Code Generation | 38.5 | 62.1 | 78.4 |
| Role Play & Narrative | 93.1 | 93.6 | 94.2 |
| Research & Analysis | 78.9 | 81.7 | 84.8 |
Performance by hardware and tool
Every hardware/tool/quantization combination Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp 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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5060 | llama.cpp | — | 39.7 tok/s | 39.2 tok/s | 16.2 GB | 131,072 tokens | 79.8 | 3 |
Benchmark runs
All 3 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp good for coding?
- In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp scores 62.1/100 for coding. It runs at about 39.2 tok/s, so if you want more speed, K2-Horizon-MoVA-36B-A4B-MLX-4bit is faster (~48.5 tok/s) and still scores well for coding (82.5/100).
- Is Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp good for agentic (tool-using) tasks?
- In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp scores 81.9/100 for agentic workflows. It runs at about 39.2 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 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp for local inference?
- Across 3 community benchmark runs, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp reaches up to 39.7 tok/s and averages 39.2 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp need?
- The leanest observed configuration used about 16.2 GB of memory.
- Which tools have been used to run Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.