Model Recipes

Qwen3.8-Flash-Next

Serve Qwen3.8-Flash-Next with Dynamo and vLLM on B200, aggregated or disaggregated.

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Each target below is a Dynamo + vLLM deployment of Qwen3.8-Flash-Next — a multimodal, ultra-sparse Mixture-of-Experts model (125B total / 6B active) with GDN+QSA hybrid attention, a 51B N-gram embedding table offloaded to host RAM, and a 262K-token context (extensible to 1M with YaRN) — with NVFP4 weights, MTP3 speculative decoding, and KV-aware routing. Pick your serving topology; every command on this page updates to match.

Choose your deployment target

Topology
Checkpoint Inferact/Qwen3.8-Flash-Next-NVFP4Precision NVFP4 weightsGPUs 4x B200 (1 node), 1 replicaParallelism TP4 + expert parallelSpec decode MTP3Routing event-driven KV-aware, prefix caching
Checkpoint Inferact/Qwen3.8-Flash-Next-NVFP4Precision NVFP4 weightsGPUs 4x B200 prefill + 4x B200 decode (1P1D, colocated)Parallelism TP4 + EP, both rolesSpec decode MTP3KV transfer NIXL over InfiniBand RDMA (rc_x + rc + cuda_copy + cuda_ipc)Routing KV-aware

Overview

Qwen3.8-Flash-Next is a multimodal, ultra-sparse Mixture-of-Experts model with 125B total parameters (including a 51B N-gram embedding table) and 6B active parameters per token. The architecture combines:

  • GDN + QSA: three of every four layers use Gated DeltaNet; the fourth uses Qwen Sparse Attention.
  • Gated Residual: four residual branches dynamically control cross-layer reads and writes.
  • N-gram Embedding: a 51B lookup memory offloaded to host RAM via VLLM_PLE_CPU_OFFLOAD=1.
  • MTP: built-in Multi-Token Prediction for speculative decoding (3 draft tokens).

This recipe uses the Inferact NVFP4 quantization (~130 GB VRAM minimum) and the upstream vllm/vllm-openai:qwen38-flash-next image with GDN/QSA kernels.

Prerequisites

  1. Dynamo Platform installed — see Kubernetes Deployment Guide.
  2. vLLM image: vllm/vllm-openai:qwen38-flash-next — model-specific build with GDN/QSA kernels. ai-dynamo is pip-installed at pod startup.
  3. Hugging Face access to Inferact/Qwen3.8-Flash-Next-NVFP4.
  4. Host memory: ≥ 51 GB per worker for N-gram embedding offload.

Quick Start

1. Create namespace and secret

$export NAMESPACE=your-namespace
$kubectl create namespace ${NAMESPACE}
$kubectl create secret generic hf-token-secret \
>  --from-literal=HF_TOKEN="your-token" \
>  -n ${NAMESPACE}

2. Create storage

Edit model-cache/model-cache.yaml and set storageClassName to a ReadWriteMany storage class.

$kubectl apply -f model-cache/model-cache.yaml -n ${NAMESPACE}

3. Download the model

$kubectl apply -f model-cache/model-download.yaml -n ${NAMESPACE}
$kubectl wait --for=condition=Complete job/model-download -n ${NAMESPACE} --timeout=7200s

4. Deploy

$# 4-GPU aggregated (1 worker × TP4)
$kubectl apply -f vllm/agg-b200-agentic/deploy.yaml -n ${NAMESPACE}
$
$# 8-GPU aggregated (2 workers × TP4)
$kubectl apply -f vllm/agg-b200-agentic-8gpu/deploy.yaml -n ${NAMESPACE}
$# 8-GPU disaggregated (1P1D, InfiniBand RDMA)
$kubectl apply -f vllm/disagg-b200-agentic/deploy.yaml -n ${NAMESPACE}

5. Smoke test

$kubectl port-forward svc/qwen38fn-agg-b200-agentic-frontend 8000:8000 -n ${NAMESPACE} &
$kubectl port-forward svc/qwen38fn-disagg-b200-agentic-frontend 8000:8000 -n ${NAMESPACE} &
$curl -s http://localhost:8000/v1/chat/completions \
>  -H 'Content-Type: application/json' \
>  -d '{
>    "model": "Inferact/Qwen3.8-Flash-Next-NVFP4",
>    "messages": [{"role": "user", "content": "Explain how Gated DeltaNet and Qwen Sparse Attention complement each other."}],
>    "max_tokens": 256
>  }'

See the recipe README for full smoke tests including tool calling and image input.