Experiment with Qwen3.8-Flash-Next 176B Model on NVIDIA GB300 NVL72 for Agentic Coding

NVIDIA provides best-effort Day 0 functional support through SGLang, vLLM, and NVIDIA TensorRT LLM, validation across NVIDIA GB300 NVL72 for inference, and post-training recipes from NVIDIA NeMo AutoModel and NVIDIA NeMo RL.

Architectural innovations for long-context inference[](#architectural_innovations_for_long-context_inference)

Qwen3.8-Flash-Next is designed for high-volume, context-intensive applications such as agentic coding, document processing, and tool-driven workflows. As context grows, attention compute and KV cache memory become bottlenecks. The model addresses both with a hybrid architecture combining Gated DeltaNet (GDN) and Qwen Sparse Attention (QSA). Three out of every four layers use GDN to continuously compress historical context into a fixed-size recurrent state, eliminating KV cache growth as sequences lengthen. The remaining layer uses QSA for precise retrieval across the full context.

Previous sparse-attention approaches rely on token-level indexers that become increasingly computationally expensive as context length grows. QSA aggregates the sequence into micro-blocks, estimates their importance at the block level, and selects only the most relevant regions. This cuts attention, compute, and indexing overhead within each layer, making the design well-suited to architectures alternating between GDN and QSA layers.

Alibaba’s published benchmarks suggest that QSA can improve the efficiency of 1M-token workloads. Compared with full attention, its attention kernel delivered speedups of up to 7.6x during prefill and 4.9x during decoding. In a cache-heavy online serving test at a 1M-token context length and with a 90% prefix-cache hit rate, Qwen3.8-Flash-Next achieved 8.6x the prefill throughput of Qwen3.7-Plus.

Figure 1. Overview of Qwen3.8-Flash-Next showing three layers of GDN and one layer of QSA with MoE to reduce memory and compute for large-context inference