Docker vllm config for Qwen3-5-122B-A10B-NVFP4

On March 22nd, someone in the LocalLLaMA community quietly posted a Docker and vLLM configuration for running Qwen3-5-122B-A10B-NVFP4 — a 122-billion-parameter mixture-of-experts model quantized to NVIDIA's FP4 format. No fanfare. No benchmark claims. Just the kind of careful, practical contribution that emerges when an engineer has spent real hours forcing a large model into a shape that actually runs. The model name alone tells a compressed story: Qwen3.5, 122B total parameters, roughly 10 billion active at any time through the MoE routing, squeezed further into FP4 precision to fit within the memory envelope of accessible NVIDIA hardware. The post has sat largely unnoticed since, which says less about its value and more about how much noise surrounds anything with a large parameter count right now.

If confirmed and reproducible, what this represents is a working deployment path for a genuinely capable model on hardware that most small teams and serious individuals can actually access. The combination of MoE architecture and FP4 quantization is specifically designed to make the parameter count misleading in the best possible way — you get the reasoning depth of a 122B model while only activating a fraction of it per token, and you store the weights at a precision that cuts memory requirements dramatically. A working Docker and vLLM config is the difference between a model existing on paper and a model running in production. The second-order effect is that community-maintained deployment configs like this one often become the quiet foundation for a dozen downstream projects — fine-tuners, API wrappers, internal tools — none of which would have started without someone doing this groundwork first.

Watch for the post gaining traction as the Qwen3.5 model family attracts broader attention, or for other community members posting reproducibility results and hardware specs. Either would meaningfully change the picture.

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