Qwen3-30B-A3B-Instruct-2507 โ W4A16 (INT4, group 128) for AMD RDNA4 / ROCm
The first 4-bit MoE quant that actually loads on vLLM + AMD RDNA4 consumer/prosumer GPUs
(Radeon AI PRO R9700, RX 9070/9070 XT โ gfx1201), as of 2026-08-14.
Why this exists
Existing community 4-bit quants of this MoE use group_size: 32, which has no compatible kernel
on vLLM ROCm (ConchLinearKernel supports group sizes [-1, 128]; Exllama needs uint4b8).
They fail at load time with Failed to find a kernel that can implement the WNA16 linear layer.
This quant uses group_size 128, symmetric INT4 (compressed-tensors pack-quantized format),
which loads and runs out of the box on the official AMD vLLM image.
Verified on
- Radeon AI PRO R9700 32 GB (gfx1201), ROCm 7.2.1 host driver
- Image:
rocm/vllm:rocm7.13.0_gfx120X-all_ubuntu24.04_py3.13_pytorch_2.10.0_vllm_0.19.1(vLLM 0.19.1) - Loads with
--max-model-len 16384โ 123,440 tokens of KV cache free on a single 32 GB card - Smoke test: coherent multilingual output, correct arithmetic
docker run -d --name vllm-moe \
--device=/dev/kfd --device=/dev/dri --group-add video \
--ipc=host --shm-size=16g --security-opt seccomp=unconfined \
-v $HOME/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 \
rocm/vllm:rocm7.13.0_gfx120X-all_ubuntu24.04_py3.13_pytorch_2.10.0_vllm_0.19.1 \
vllm serve evilbb2/Qwen3-30B-A3B-Instruct-2507-W4A16-g128-RDNA4 --max-model-len 16384
How it was made
Shard-by-shard data-free RTN (round-to-nearest) symmetric quantization, scale = absmax/7,
groups of 128 along the input dimension, packed with compressed_tensors.pack_to_int32.
Quantized: all q/k/v/o_proj, gate/up/down_proj (experts included).
Kept in original precision: MoE router gates, lm_head, embeddings, norms.
Honest caveats
- No calibration data was used (pure RTN). Expect some quality loss versus a calibrated AWQ/GPTQ quant. Structured quality benchmarks are in progress and this card will be updated.
- Experimental community quant โ not affiliated with Qwen/Alibaba.
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Model tree for evilbb2/Qwen3-30B-A3B-Instruct-2507-W4A16-g128-RDNA4
Base model
Qwen/Qwen3-30B-A3B-Instruct-2507