Qwen/Qwen3.6-35B-A3B

Smaller Qwen3.6 multimodal MoE model (35B total / 3B active) with 256 experts (8 routed + 1 shared), gated delta networks architecture, and 262K context

View on HuggingFace

moe35B / 3B262,144 ctxvLLM 0.17.0+multimodaltext

Guide

Overview

Qwen3.6-35B-A3B is the smaller sibling of Qwen3.5, sharing the same gated-delta-networks MoE architecture but with 35B total parameters and 3B activated (256 experts, 8 routed + 1 shared). With FP8 weights it fits comfortably on a single 80 GB GPU and supports the full 262K context.

Prerequisites

vLLM version: >= 0.17.0
Hardware (BF16): 1x H200 or 2x H100
Hardware (FP8): single H100/H200 or 1x MI300X/MI325X/MI355X

Install vLLM

uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend=auto

Launching the Server

Single-GPU FP8

vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

BF16 on 2xH200 (TP2)

vllm serve Qwen/Qwen3.6-35B-A3B \
  --tensor-parallel-size 2 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

MTP speculative decoding

vllm serve Qwen/Qwen3.6-35B-A3B \
  --tensor-parallel-size 2 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --speculative-config '{"method": "mtp", "num_speculative_tokens": 2}'

AMD (MI300X / MI325X / MI355X)

VLLM_ROCM_USE_AITER=1 vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --trust-remote-code

Processing Ultra-Long Texts

Qwen3.6-35B-A3B natively supports 262,144 tokens. For longer inputs, apply YaRN RoPE scaling via --hf-overrides and raise --max-model-len. Pick factor to match your real workload — 2.0 covers ~524K, 4.0 covers ~1M — since YaRN at higher factors degrades short-context quality.

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve Qwen/Qwen3.6-35B-A3B-FP8 \
  --tensor-parallel-size 2 \
  --max-model-len 1010000 \
  --reasoning-parser qwen3 \
  --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}'

See the model card for the full parameter reference.

Client Usage

from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
resp = client.chat.completions.create(
    model="Qwen/Qwen3.6-35B-A3B",
    messages=[{"role": "user", "content": "Explain gated delta networks in one paragraph."}],
    max_tokens=512,
)
print(resp.choices[0].message.content)

Troubleshooting

CUDA graph / Mamba cache size error: reduce --max-cudagraph-capture-size (default 512). See vLLM PR #34571.
Reasoning disable: add --default-chat-template-kwargs '{"enable_thinking": false}'.
Prefix Caching (Mamba): currently experimental in "align" mode.