Qwen Flash Next
A lightweight, efficiency-oriented model in Alibaba's Qwen series, designed to achieve high inference performance at a relatively small parameter scale. It supports deployment in multiple quantization formats.
Timeline (last 90 days)
The official team provides corresponding GGUF quantized model files on Hugging Face, with the repository address at ggml-org/Qwen3.8-Flash-Next-GGUF.
Specific performance improvement figures, compatible hardware ranges, and actual MTP acceleration ratios still require confirmation from official documentation and subsequent community benchmarking.
Work on MTP (multi-token prediction) support for the Qwen Flash Next model has been restarted.
Developers have prepared switchable quantized models for Qwen Flash Next and submitted a corresponding Pull Request to the llama.cpp repository.
The MTP support work is currently still in a WIP (work in progress) state and has not yet stabilized.
The quantized model for Qwen Flash Next is hosted on Hugging Face in the ggml-org/Qwen3.8-Flash-Next-GGUF repository, providing weight files in GGUF format.
A Reddit user used Codex to conduct a comparative evaluation of the Qwen Flash Next IQ4_XS quantized version against the Qwen3 27B FP8 version.
On the MMLU-Pro benchmark, Flash-Next IQ4_XS scored 83.8% and Qwen3 27B FP8 scored 75.0%.
On GSM8K, Flash-Next scored 97.5% versus FP8's 90.0%; on German MGSM, 92.5% versus 90.0%; and on IFEval, both tied at 89.6%.
To complete the 250-case test, FP8 27B took 36 minutes and 27 seconds, while Flash-Next IQ4_XS took 2 hours, 5 minutes, and 47 seconds, a difference of approximately 3.5 times.
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All Facts (5)
Specific performance improvement figures, compatible hardware ranges, and actual MTP acceleration ratios still require confirmation from official documentation and subsequent community benchmarking.
50%UnverifiedThe tester recommends choosing the lower-quantized Flash Next for higher accuracy in single-user scenarios, and choosing FP8 27B for concurrent processing since its speed is approximately 4 times that of QFN.
50%UnverifiedAMD's RX 9700 series GPUs have hardware-level acceleration support for mxfp4, and upgrading to 128GB of memory and using the mxfp4 quantized version of Flash Next can increase QFN speed by approximately 2 times.
50%UnverifiedThe official team provides corresponding GGUF quantized model files on Hugging Face, with the repository address at ggml-org/Qwen3.8-Flash-Next-GGUF.
50%UnverifiedThe quantized model for Qwen Flash Next is hosted on Hugging Face in the ggml-org/Qwen3.8-Flash-Next-GGUF repository, providing weight files in GGUF format.
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