litert-community/Qwen3-0.6B

This repository contains LiteRT-LM variants of Qwen3-0.6B for Android and desktop deployment.

Available Artifacts

File	Quantization	Context	Size
`Qwen3-0.6B.litertlm`	dynamic INT8 weights, float KV	4096	586 MB
`Qwen3-0.6B.mediatek.mt6993.litertlm`	a16w8 NPU-targeted	4096	992 MB
`qwen3_0_6b_mixed_int4.litertlm`	TorchAO mixed INT4, float KV	2048	474.61 MiB

Conversion Notes

The mixed INT4 .litertlm artifact was produced with a TorchAO-based quantize-first recipe from the original Hugging Face checkpoint. This is a mixed quantization layout rather than a uniform all-INT4 model: eligible linear projection weights are stored as blockwise INT4 with group size 32 and floating-point scales, token embedding weights use weight-only INT8 quantization, and normalization/reduction paths plus KV cache tensors remain floating point.

The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including odml.runtime_bmm and odml.cache_update.

Qwen3-0.6B.litertlm is a separate dynamic INT8 artifact. It was converted through the LiteRT Torch (litert-torch) path and quantized with AI Edge Quantizer. This artifact is independent from qwen3_0_6b_mixed_int4.litertlm, which uses the TorchAO-based mixed INT4 recipe described above.

Android Performance Examples

These are representative measurements from retail devices to give a rough sense of on-device runtime behavior, not a direct comparison between hardware platforms. All numbers were collected with LiteRT-LM's litert_lm_advanced_main launched from an adb command line on the connected device; they are not app-level measurements from an integrated Android application.

Hardware benchmark disclosure: Results were measured by us on retail devices purchased through normal channels. These results are not affiliated with, sponsored by, endorsed by, or verified by Samsung, vivo, Qualcomm, MediaTek, Google, MLCommons, or Hugging Face. Results depend on device SKU, OS build, thermal state, battery mode, backend, model quantization, runtime version, and benchmark settings.

`qwen3_0_6b_mixed_int4.litertlm`

Context: 2048. Shape: 256 prefill tokens / 256 decode tokens. Rows use LiteRT-LM v0.13.1. Values report the warmed iteration from a two-iteration run unless noted.

Example device	Backend	Prefill (tok/s)	Decode (tok/s)	TTFT (s)	Peak Private Footprint
Samsung SM-S937U1	GPU OpenCL	1844.95	69.38	0.150	585 MB
vivo V2502A	GPU OpenCL	1055.89	22.34	0.285	1856 MB
TECNO LJ9	GPU OpenCL	637.01	33.51	0.430	1832 MB
Samsung SM-S937U1	CPU	576.59	12.90	0.520	2895 MB
TECNO LJ9	CPU	231.15	8.33	1.230	2890 MB

`Qwen3-0.6B.litertlm`

Context: 4096. Samsung and TECNO rows use 256 prefill tokens / 256 decode tokens with LiteRT-LM v0.13.1. The vivo rows are previously published 4096-context reference results; TTFT, peak footprint, and exact prompt/decode shape were not recorded in this update.

Example device	Backend	Prefill (tok/s)	Decode (tok/s)	TTFT (s)	Peak Private Footprint
Samsung SM-S937U1	GPU OpenCL	646.33	25.31	0.440	2940 MB
TECNO LJ9	GPU OpenCL	254.24	12.10	1.090	4283 MB
vivo V2502A	GPU OpenCL	580	21	-	-
Samsung SM-S937U1	CPU	212.07	13.02	1.280	2697 MB
TECNO LJ9	CPU	95.14	9.32	2.800	2699 MB
vivo V2502A	CPU	165	9	-	-

`Qwen3-0.6B.mediatek.mt6993.litertlm`

Context: 4096. This is a previously published MediaTek MT6993 NPU reference result; TTFT, peak footprint, and exact prompt/decode shape were not recorded in this update.

Example device	Backend	Prefill (tok/s)	Decode (tok/s)	TTFT (s)	Peak Private Footprint
vivo V2502A	NPU	1472	36	-	-

Desktop Smoke Benchmark

Benchmarked on AMD Radeon AI PRO R9700 via LiteRT-LM WebGPU with 256 prefill tokens and 32 decode tokens.

Backend	Prefill (tok/s)	Decode (tok/s)	TTFT (s)	Peak Private Footprint
GPU WebGPU	4257.13	142.07	0.07	803 MB

Try It

Install uv and run:

uv tool install litert-lm
uvx litert-lm run --from-huggingface-repo=litert-community/Qwen3-0.6B qwen3_0_6b_mixed_int4.litertlm --prompt="What is the capital of France?"

Integration

Ready to integrate this into your product? Get started in the LiteRT-LM documentation.

Citation

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report},
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388},
}