Model benchmarks
unsloth/LFM2.5-VL-1.6B-GGUF local LLM performance
As of September 2026, unsloth/LFM2.5-VL-1.6B-GGUF runs at up to 1101.6 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
1.6B
Peak speed
1101.6 tok/s
Average speed
1053.0 tok/s
Avg PP
1191.7 tok/s
Min memory
800 MB
Max context
8.192 tokens
Avg output / run
15.242 tokens
Avg runtime / run
24s
Avg quality
18.4
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 | 14.4 | 18.4 | 22.5 |
| Agent Workflow | 13.4 | 15.7 | 17.9 |
| Code Generation | 0.5 | 4.8 | 9.0 |
| Role Play & Narrative | 19.2 | 31.7 | 44.1 |
| Research & Analysis | 19.9 | 21.7 | 23.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/LFM2.5-VL-1.6B-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 | — | 1101.6 tok/s | 1053.0 tok/s | 800 MB | 8.192 tokens | 18.4 | 2 |
Benchmark runs
All 2 unsloth/LFM2.5-VL-1.6B-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-VL-1.6B-GGUF for local inference?
- Across 2 community benchmark runs, unsloth/LFM2.5-VL-1.6B-GGUF reaches up to 1101.6 tok/s and averages 1053.0 tok/s, with the fastest results on NVIDIA GeForce RTX 4070.
- How much memory does unsloth/LFM2.5-VL-1.6B-GGUF need?
- The leanest observed configuration used about 800 MB of memory.
- Which tools have been used to run unsloth/LFM2.5-VL-1.6B-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.