Qwen3.8-Flash-Next

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What Qwen3.8-Flash-Next is

Model Overview Description: RadixArk Qwen3.8-Flash-Next-NVFP4 is the quantized version of Qwen/Qwen3.8-Flash-Next, a hybrid-architecture multimodal Mixture-of-Experts model. Quantization was performed with NVIDIA Model Optimizer (snapshot 87c9f8cf) using the NVFP4 W4A4 recipe, scoped to the routed experts only. This checkpoint is a private candidate release. References NVIDIA Model Optimizer: https://github.com/NVIDIA/TensorRT-Model-Optimizer License/Terms of Use: See the source model for license terms. Use Case: Developers evaluating NVFP4-quantized serving of Qwen3.8-Flash-Next for agentic systems, chat, coding, and multimodal reasoning workloads. Release Date: Hugging Face on 08/25/2026 via https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4 Model Architecture: Architecture Type: Transformer (hybrid GDN + QSA sparse attention, multi-hyperconnection streams, PLE n-gram injection) <br Network Architecture: Multimodal MoE — 48 decoder layers, 512 routed experts per MoE layer (top-10 routing) + shared expert, 1 MTP layer <br Number of Model Parameters: ~180B in total (360 GB BF16 source) <br Input: Input Type(s): Text, Image, Video <br Input Format(s): String, RGB, Video <br Other Properties Related to Input: Context length up to 262K <br Output: Output Type(s): Text <br Output Format: String <br Software Integration: Supported Runtime Engine(s): <br SGLang (with qwen4exp model support) <br Supported Hardware Microarchitecture Compatibility: <br NVIDIA Blackwell (validated on GB300 and B300) <br Preferred Operating System(s): <br Linux <br Model Version(s): NVFP4 candidate 1.0, quantized with nvidia-modelopt v0.46.0 (snapshot 87c9f8cf83021957d1a1a575c90c9a4eaaf7ef0c). Calibration Dataset: Link: cnndailymail (config 3.0.0, train split) <br Properties: 128 articles (seed 1234), truncated to 512 tokens; MoE-block input activations captured from live SGLang serving (prefill only), 62,139 rows per layer; activation scales by max calibration over 8 seeded sampled batches per part. Representativeness probe: GSM8K train [0:16] x2. <br Post Training Quantization This model was obtained by quantizing Qwen3.8-Flash-Next to NVFP4, ready for inference with SGLang. Only t

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Call it like any OpenAI endpoint

Once AxForge deploys radixark-qwen3-8-flash-next for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (radixark-qwen3-8-flash-next below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"radixark-qwen3-8-flash-next","messages":[{"role":"user","content":"Hello"}]}'

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