Model ID
Qwen/Qwen3.8-Flash-Next
Capabilities
Qwen3.8 Flash Next
Qwen3.8 Flash Next is a 125B parameter hybrid model with 6B activated parameters, along with 51B n-gram embedding and 4B MTP parameters, designed for efficient long-context and agentic workloads. Its architecture combines Gated DeltaNet with Qwen Sparse Attention, 512 experts with 10 routed and 1 shared expert, Gated Residuals, and n-gram embeddings for efficient scaling and inference. It supports 262K-token context, extensible to 1M tokens, with native vision and video understanding. The model targets efficient deployment while reducing long-context latency and memory demands.
About this model
Qwen3.8 Flash Next is a 125B parameter hybrid model with 6B activated parameters, along with 51B n-gram embedding and 4B MTP parameters, designed for efficient long-context and agentic workloads. Its architecture combines Gated DeltaNet with Qwen Sparse Attention, 512 experts with 10 routed and 1 shared expert, Gated Residuals, and n-gram embeddings for efficient scaling and inference. It supports 262K-token context, extensible to 1M tokens, with native vision and video understanding. The model targets efficient deployment while reducing long-context latency and memory demands.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3.8-Flash-Next). Released under Qwen Community 1.0. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Requires vllm/vllm-openai:qwen38-flash-next or any standard vLLM image v0.28.0+ to support this model configuration.
Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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