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).

llama.cpp
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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.

TaskP5 (low)AvgP95 (high)
Overall11.413.916.4
Agent Workflow11.419.427.6
Code Generation0.00.00.0
Role Play & Narrative18.419.921.6
Research & Analysis12.816.318.6

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.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
Intel(R) Arc(TM) 140V GPU (16GB)llama.cpp359.5 tok/s303.5 tok/s15 MB128.000 tokens13.93

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.