---
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE
base_model: LiquidAI/LFM2.5-230M
tags:
  - qualcomm
  - hexagon
  - npu
  - qnn
  - qhexrt
  - on-device
  - lfm2
language:
  - en
pipeline_tag: text-generation
---

# LFM2.5-230M — Hexagon v81 NPU (QHexRT)

[LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) running fully on the **Qualcomm Hexagon
v81 NPU** (Snapdragon 8 Elite Gen-2 / SM8850) via **QHexRT** — RunAnywhere's inference engine for
Qualcomm NPUs. **100% on the HTP.** No Python in the hot path. W8 weight-only, GQA-native decode,
batched prefill, on-NPU lm-head.

QHexRT is the first engine built to run **LLM, VLM, STT, TTS, and embeddings** fully on Qualcomm
Hexagon NPUs. LFM 2.5 230M is the first model in the catalog.

## Why the NPU — measured on SM8850 (vs llama.cpp CPU, same device)

| metric | Hexagon v81 NPU | CPU (llama.cpp Q8_0) | NPU advantage |
|---|---|---|---|
| **Prefill** | **12,540 tok/s** | 871 tok/s | **~14× faster** |
| **Time-to-first-token** (512-token prompt) | **~36 ms** (flat) | 588 ms | **~16× lower** |
| **End-to-end** (512-token prompt + 128 new) | **0.77 s** | 1.13 s | **~1.5× faster** |

Batched **O(1) prefill** holds TTFT flat at **~36 ms regardless of prompt length**, so the NPU pulls further
ahead the longer the context — at far lower power than driving 8 CPU cores at max clock.

**Full launch write-up:** [runanywhere.ai/blog](https://www.runanywhere.ai/blog/qhexrt-npu-vs-cpu-lfm2.5-230m-qualcomm-hexagon) · [article draft in this repo](blog/qhexrt-npu-vs-cpu-lfm2.5-230m.md)

## Run

```bash
hf download runanywhere/lfm2_5_230m_HNPU --local-dir lfm2_5_230m_HNPU
adb push lfm2_5_230m_HNPU/v81 /data/local/tmp/lfm230     # PowerShell + native paths on Windows
adb shell "cd /data/local/tmp/lfm230 && LD_LIBRARY_PATH=. \
  ./qhx_generate lfm2-5-230m.json libQnnHtp.so libQnnSystem.so . 64 'The capital of France is'"
```

Stage the QAIRT v81 runtime libs (`libQnnHtp.so`, `libQnnSystem.so`, `libQnnHtpV81Skel.so`/`Stub.so`) + the
`qhx_generate` tool into the same dir (from the QAIRT SDK). Context binaries are **arch-pinned to v81**.
Contact [san@runanywhere.ai](mailto:san@runanywhere.ai) for QHexRT deployment access.

## `v81/`
`lfm2-5-230m.json` (manifest) · `lfm230_dec_512_w8.bin` (decode) · `lfm230_pf_512_w8.bin` (prefill) ·
`lfm230_lmh_w8.bin` (lm-head) · `lfm_embed_f16.bin` (embeddings) · `tokenizer.json`