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 3 community benchmark runs across 1 GPU).
Model size
1.2B
Peak speed
434.6 tok/s
Average speed
367.0 tok/s
Avg PP
1164.5 tok/s
Min memory
600 MB
Max context
128.072 tokens
Avg output / run
5.208 tokens
Avg runtime / run
17s
Avg quality
35.5
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 | 32.9 | 35.5 | 38.3 |
| Agent Workflow | 63.0 | 65.5 | 68.4 |
| Code Generation | 3.6 | 7.0 | 12.5 |
| Role Play & Narrative | 38.4 | 49.3 | 59.9 |
| Research & Analysis | 17.4 | 20.3 | 24.6 |
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.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 4070 | llama.cpp | — | 434.6 tok/s | 367.0 tok/s | 600 MB | 128.072 tokens | 35.5 | 3 |
Benchmark runs
All 3 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 3 community benchmark runs, unsloth/LFM2.5-1.2B-Instruct-GGUF reaches up to 434.6 tok/s and averages 367.0 tok/s, with the fastest results on NVIDIA GeForce RTX 4070.
- How much memory does unsloth/LFM2.5-1.2B-Instruct-GGUF need?
- The leanest observed configuration used about 600 MB of memory.
- Which tools have been used to run unsloth/LFM2.5-1.2B-Instruct-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.