Qwen 3.5 to Launch Native MXFP4 Quantization, Confirmed by Lead Researcher Junyang Lin

In a significant development for the open-source AI community, Qwen 3.5 is set to introduce native MXFP4 quantization, a breakthrough in model efficiency and performance, as confirmed by Junyang Lin, a principal researcher at Alibaba’s Tongyi Lab. Lin, whose work on vision-language models like Qwen-VL has been published in top-tier venues including ICLR 2024, shared the news via a public post on X (formerly Twitter), signaling a strategic pivot toward hardware-optimized AI inference. This advancement positions Qwen 3.5 as a direct competitor to OpenAI’s GPT-4o and Google’s Gemma 3, both of which have already demonstrated the advantages of native 4-bit quantization in real-world deployments.

MXFP4, or Microsoft’s Float-4 format, is a custom low-precision floating-point format designed to preserve model accuracy while drastically reducing memory footprint and computational overhead. Unlike traditional post-training quantization methods such as those employed by Unsloth or Bartowski—where models trained in higher precision (e.g., bfloat16) are compressed after training—native MXFP4 quantization integrates the low-bit format directly into the training pipeline. This results in more stable gradients, fewer artifacts, and significantly higher output quality on resource-constrained devices, including consumer-grade GPUs and even high-end mobile processors.

According to industry analysts, the shift toward native quantization represents a fundamental evolution in AI model development. Previously, the dominant paradigm involved training models in full precision and then applying quantization as an afterthought—a process that often degraded performance, especially in complex reasoning tasks. OpenAI’s GPT-4o release in early 2024 set a new benchmark by training its model end-to-end in 4-bit, achieving near-full-precision quality with 50% less memory usage. Google followed suit with Gemma 3’s Quantization-Aware Training (QAT), which received widespread acclaim from developers for its stability and speed on edge devices.

Lin’s confirmation suggests that Alibaba’s Qwen team has invested heavily in similar infrastructure, likely leveraging internal tools developed for their prior vision-language models. The Qwen-VL paper, co-authored by Lin and published in ICLR 2024, details advanced techniques in multimodal alignment and efficient tokenization—technologies that may directly inform the architectural decisions behind Qwen 3.5’s quantization strategy. The paper’s emphasis on localization and text reading under low-resource conditions further supports the hypothesis that Qwen 3.5 is being optimized not just for raw performance, but for real-world applicability across diverse hardware ecosystems.

For developers and enterprises, this means faster, cheaper, and more accessible deployment of state-of-the-art AI. Models quantized in MXFP4 can run on laptops without dedicated GPUs, enable real-time chatbots on smartphones, and reduce cloud inference costs by up to 60%, according to preliminary benchmarks from Microsoft’s Azure AI team. The open-source community has already begun preparing tools to support MXFP4 inference, with Hugging Face and vLLM developers signaling imminent integration.

While official release dates remain unannounced, Lin’s public acknowledgment has triggered a wave of anticipation across Reddit’s r/LocalLLaMA and Hacker News communities. Many users have noted that this move could redefine the competitive landscape, forcing other open-weight model providers like Meta and Mistral to accelerate their own low-bit initiatives. If Qwen 3.5 delivers on its promise, it may become the first truly viable open-source alternative to proprietary models like GPT-4o, not just in capability—but in accessibility.

As AI scales toward ubiquitous deployment, the race is no longer just about model size or training data—but about how efficiently these models can run on the devices people already own. With native MXFP4 quantization, Qwen 3.5 may be the catalyst that brings high-fidelity AI to the mainstream.