Model benchmarks

unsloth/LFM2.5-1.2B-Instruct-GGUF local LLM performance

As of September 2026, unsloth/LFM2.5-1.2B-Instruct-GGUF runs at up to 434.6 tok/s for local inference (best of 7 community benchmark runs across 2 GPUs).

Unsloth Studiollama.cppQ4_K_M
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Model size

1.2B

Peak speed

434.6 tok/s

Average speed

310.7 tok/s

Avg PP

1572.0 tok/s

Min memory

n/a

Max context

128,072 tokens

Avg output / run

5,155 tokens

Avg runtime / run

27s

Avg quality

36.7

Benchmark runs

7

GPUs tested

2

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 7 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)
Overall30.536.744.6
Agent Workflow40.664.284.5
Code Generation3.56.311.5
Role Play & Narrative39.954.065.6
Research & Analysis17.622.226.2

Performance by hardware and tool

Every hardware/tool/quantization combination unsloth/LFM2.5-1.2B-Instruct-GGUF has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
NVIDIA GeForce RTX 4070llama.cpp—434.6 tok/s359.2 tok/sn/a128,072 tokens34.24
NVIDIA GeForce RTX 4070Unsloth StudioQ4_K_M335.2 tok/s334.3 tok/sn/a65,536 tokens38.32
Intel(R) Arc(TM) 140V GPU (16GB)Unsloth Studio—69.4 tok/s69.4 tok/sn/a128,000 tokens43.71

Benchmark runs

All 7 unsloth/LFM2.5-1.2B-Instruct-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Frequently asked questions

How fast is unsloth/LFM2.5-1.2B-Instruct-GGUF for local inference?
Across 7 community benchmark runs, unsloth/LFM2.5-1.2B-Instruct-GGUF reaches up to 434.6 tok/s and averages 310.7 tok/s, with the fastest results on NVIDIA GeForce RTX 4070.
Which tools have been used to run unsloth/LFM2.5-1.2B-Instruct-GGUF?
Benchmarks were submitted using Unsloth Studio, llama.cpp. Results are community-contributed and updated as new runs arrive.