vLLM/Recipes
Qwen

Qwen/Qwen3.5-122B-A10B

Mid-size Qwen3.5 multimodal MoE (122B total / 10B active) with gated delta networks, 256 experts, and 262K context

Qwen3.5 mid-tier MoE — fits on 4x H200 BF16 or 2x H200 FP8

View on HuggingFace
moe122B / 10B262,144 ctxvLLM 0.17.0+multimodaltext
Guide

Overview

Qwen3.5-122B-A10B is a mid-tier member of the Qwen3.5 family, sharing the gated delta networks MoE architecture with 122B total parameters and 10B activated per token (256 experts). It is multimodal (vision + text) and natively supports 262K context.

Prerequisites

  • vLLM version: >= 0.17.0
  • Hardware (BF16): 4x H200 or 8x H100
  • Hardware (FP8): 2x H200 or 4x H100
  • Hardware (Int4): single 80 GB GPU

Install vLLM

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

Launching the Server

BF16 on 4xH200 (TP4)

vllm serve Qwen/Qwen3.5-122B-A10B \
  --tensor-parallel-size 4 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

FP8 on 2xH200 (TP2)

vllm serve Qwen/Qwen3.5-122B-A10B-FP8 \
  --tensor-parallel-size 2 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

Throughput-focused (text-only, EP)

vllm serve Qwen/Qwen3.5-122B-A10B-FP8 \
  -dp 4 --enable-expert-parallel \
  --language-model-only \
  --reasoning-parser qwen3 \
  --enable-prefix-caching

MTP speculative decoding

vllm serve Qwen/Qwen3.5-122B-A10B-FP8 \
  --tensor-parallel-size 2 \
  --speculative-config '{"method": "mtp", "num_speculative_tokens": 1}' \
  --reasoning-parser qwen3

AMD (MI325X / MI355X, single GPU, FP8)

The FP8 checkpoint fits one MI300X-class GPU. Three settings matter on ROCm and none is on by default: VLLM_ROCM_USE_AITER=1 switches the MoE to the AITER FP8 kernels, --attention-backend ROCM_AITER_FA keeps decode flat across context (the auto-selected ROCM_ATTN decays with prompt length on this hybrid GDN model), and --kv-cache-dtype fp8 keeps MTP flat across context too.

VLLM_ROCM_USE_AITER=1 vllm serve Qwen/Qwen3.5-122B-A10B-FP8 \
  --attention-backend ROCM_AITER_FA \
  --kv-cache-dtype fp8 --max-num-seqs 64 \
  --reasoning-parser qwen3

--max-num-seqs 64 because the default exceeds the Mamba block budget on 192 GB. MTP (--speculative-config '{"method":"mtp","num_speculative_tokens":1}') works with the same flags. Single-stream decode (tokens/s at ~200 / ~2k / ~12k / ~30k prompt tokens, 128 output tokens, prefix caching off), measured 2026-10-07 on vllm/vllm-openai-rocm:v0.31.0:

MI325XMI355X
AITER + ROCM_AITER_FA128 / 127 / 127 / 127143 / 143 / 143 / 143
+ MTP184 / 169 / 172 / 164204 / 193 / 195 / 190

This recipe used ROCM_AITER_UNIFIED_ATTN before v0.31.0. Avoid it on MI350X/MI355X with v0.31.0: there it runs AITER's new gfx950 Gluon kernel, which decodes at 110 tokens/s and, with MTP, falls from 200 to 72 tokens/s between ~200 and ~30k prompt tokens (vllm-project/vllm#60408). MI325X is unaffected (126 tokens/s; 182 to 162 with MTP).

Client Usage

from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
resp = client.chat.completions.create(
    model="Qwen/Qwen3.5-122B-A10B",
    messages=[{"role": "user", "content": "Summarize the gated delta networks paper."}],
    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.
  • Disable reasoning: add --default-chat-template-kwargs '{"enable_thinking": false}'.
  • Prefix Caching (Mamba): currently experimental in "align" mode.

References