Model benchmarks
Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp local LLM performance
As of September 2026, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp runs at up to 38.3 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
38.3 tok/s
Average speed
37.9 tok/s
Avg PP
296.8 tok/s
Min memory
13.2 GB
Max context
98.304 tokens
Avg output / run
33.028 tokens
Avg runtime / run
14m 33s
Avg quality
82.4
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 | 78.3 | 82.4 | 86.6 |
| Agent Workflow | 72.1 | 78.9 | 85.6 |
| Code Generation | 68.4 | 75.6 | 82.8 |
| Role Play & Narrative | 87.2 | 89.8 | 92.4 |
| Research & Analysis | 85.3 | 85.4 | 85.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-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 | — | 38.3 tok/s | 37.9 tok/s | 13.2 GB | 98.304 tokens | 82.4 | 2 |
Benchmark runs
All 2 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-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_S-mtp good for coding?
- In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp scores 75.6/100 for coding. It runs at about 37.9 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 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp good for agentic (tool-using) tasks?
- In our benchmarks, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp scores 78.9/100 for agentic workflows. It runs at about 37.9 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 Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp for local inference?
- Across 2 community benchmark runs, Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp reaches up to 38.3 tok/s and averages 37.9 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp need?
- The leanest observed configuration used about 13.2 GB of memory.
- Which tools have been used to run Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.