Model benchmarks
unsloth/LFM2.5-230M-GGUF local LLM performance
As of September 2026, unsloth/LFM2.5-230M-GGUF runs at up to 359.5 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
30M
Peak speed
359.5 tok/s
Average speed
303.5 tok/s
Avg PP
1059.3 tok/s
Min memory
15 MB
Max context
128.000 tokens
Avg output / run
4.015 tokens
Avg runtime / run
16s
Avg quality
13.9
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 | 11.4 | 13.9 | 16.4 |
| Agent Workflow | 11.4 | 19.4 | 27.6 |
| Code Generation | 0.0 | 0.0 | 0.0 |
| Role Play & Narrative | 18.4 | 19.9 | 21.6 |
| Research & Analysis | 12.8 | 16.3 | 18.6 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/LFM2.5-230M-GGUF 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 |
|---|---|---|---|---|---|---|---|---|
| Intel(R) Arc(TM) 140V GPU (16GB) | llama.cpp | — | 359.5 tok/s | 303.5 tok/s | 15 MB | 128.000 tokens | 13.9 | 3 |
Benchmark runs
All 3 unsloth/LFM2.5-230M-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- How fast is unsloth/LFM2.5-230M-GGUF for local inference?
- Across 3 community benchmark runs, unsloth/LFM2.5-230M-GGUF reaches up to 359.5 tok/s and averages 303.5 tok/s, with the fastest results on Intel(R) Arc(TM) 140V GPU (16GB).
- How much memory does unsloth/LFM2.5-230M-GGUF need?
- The leanest observed configuration used about 15 MB of memory.
- Which tools have been used to run unsloth/LFM2.5-230M-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.