Model reference · open weights
Fun-ASR-Nano is an open-weight audio or speech model from FunAudioLLM. Fun-ASR-Nano-GGUF (GGUF) weighs 805 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the FunAudioLLM/Fun-ASR-Nano-GGUF model card, 2026-10-01
What it is
| Released by | FunAudioLLM |
|---|---|
| Released | 2026-06-20 |
| VRAM | 805 MB for the weights |
What it runs on
| Card | One stream | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
GGUF build of Fun-ASR-Nano (SenseVoice SAN-M encoder + adaptor + Qwen3-0.6B LLM decoder) for the zero-Python, CPU/edge FunASR llama.cpp runtime — the accuracy leader (LLM decoder), single C++ binary.
The Fun-ASR-Nano LLM (Qwen3-0.6B) ships in three tiers — all within 0.1% CER (184-file micro-CER). Pair any with funasr-encoder-f16.gguf (470 MB).
| LLM file | size | CER ↓ | speed |
|---|---|---|---|
qwen3-0.6b-q4km.gguf | 484 MB | 8.35% | 6.1× |
qwen3-0.6b-q5km.gguf | 551 MB | 8.25% | 5.7× |
qwen3-0.6b-q8_0.gguf | 805 MB | 8.30% | 6.0× |
Recommended: q4_K_M (smallest) or q5_K_M (best).
These are GGUF weights for the FunASR llama.cpp runtime — a whisper.cpp-style, single self-contained binary for CPU / edge. Grab a prebuilt binary, then fetch this model and run:
runtime-llamacpp-v*)bash download-funasr-model.sh nano ./gguf
llama-funasr-cli --enc ./gguf/funasr-encoder-f16.gguf -m ./gguf/qwen3-0.6b-q8_0.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav
| file | size | notes |
|---|---|---|
funasr-encoder-f16.gguf | 470 MB | audio encoder + adaptor (f16) |
qwen3-0.6b-q8_0.gguf | 805 MB | LLM decoder, recommended (Q8_0) |
qwen3-0.6b-q4km.gguf | 484 MB | LLM decoder, smaller (Q4_K_M) |
llama-funasr-cli --enc funasr-encoder-f16.gguf -m qwen3-0.6b-q8_0.gguf -a audio.wav --vad fsmn-vad.gguf
On CPU: 8.30 % CER on the 184-clip Mandarin benchmark (vs whisper.cpp 22–31 %).
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works