Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding | NVIDIA Technical Blog

This post details the experimental use of Qwen3.8-Flash-Next, a multimodal MoE model with a 125B parameter main model and a 262,144-token context window (extensible to 1M tokens), on the NVIDIA GB300 NVL72 platform for agentic coding applications. It highlights the model's architectural innovations, Gated DeltaNet (GDN) and Qwen Sparse Attention (QSA), which address KV cache growth and attention bottlenecks for long contexts, achieving significant speedups over full attention. The post quantifies performance on the GB300 NVL72, reporting over 16K tokens/sec/GPU and over 200 tokens/sec/user. It also mentions fine-tuning with NVIDIA NeMo AutoModel and reinforcement learning with NeMo RL recipes, and provides integration details with inference engines like SGLang, vLLM, and TokenSpeed.