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aweussom/SmolLM3-3B-int8-cw-ov
SmolLM3-3B-int8-cw-ov is a machine learning model from aweussom. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
INT8 channel-wise symmetric OpenVINO export of HuggingFaceTB/SmolLM3-3B. The quality-first companion to the int4-cw build: channel-wise int4 is the lossiest int4 variant (the NPU can't compile group-quantized int4 — s…
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From the Hugging Face model README
INT8 channel-wise symmetric OpenVINO export of HuggingFaceTB/SmolLM3-3B. The quality-first companion to the int4-cw build: channel-wise int4 is the lossiest int4 variant (the NPU can't compile group-quantized int4 — see the int4-cw card), so if you have the memory and can take ~half the decode speed, int8 keeps more of the model.
optimum-cli export openvino --model HuggingFaceTB/SmolLM3-3B \
--weight-format int8 --sym --group-size -1 <output_dir>
Toolchain: OpenVINO 2026.1.0, optimum-intel 1.27.0, transformers 4.57.6. Verified on openvino-genai 2026.3.0 runtime.
| Device | Decode | vs int4-cw |
|---|---|---|
| NPU | 12.3 tok/s | 23.3 |
| iGPU | 16.8 tok/s | 29.7 |
| CPU | 22.7 tok/s | 37.5 |
Note: int8-sym works on SmolLM3 but is NOT a general NPU recipe — on LFM2-family models it produces garbage output (and int8-asym runs correct but ~1 tok/s on NPU). Test per-architecture before assuming.
import openvino_genai as og
pipe = og.LLMPipeline("SmolLM3-3B-int8-cw-ov", "NPU", MAX_PROMPT_LEN=4096)
print(pipe.generate("Hello!", og.GenerationConfig(max_new_tokens=64)))
Or with NoLlama:
python nollama.py --model-dir ~/models/SmolLM3-3B-int8-cw-ov.