Qwen3.8-Flash-Next Best Practice

Qwen3.8-Flash-Next is a multimodal, ultra-sparse MoE model with 125B parameters in total (including an additional 51B N-gram embedding table), activating about 6B per token. Its architecture combines three key designs:

Qwen3.8-Flash-Next architecture

GDN + QSA: three of every four layers use Gated DeltaNet to compress history, while the fourth uses Qwen Sparse Attention for precise long-range retrieval. QSA’s budget is 2048 tokens.
Gated Residual (Hyper-Connections): each layer’s input is expanded into 4 residual branches that dynamically control cross-layer reads and writes.
N-gram Embedding: a 51B lookup-memory table that trades an extremely small per-token compute cost for capacity, and can be offloaded to host memory.

The checkpoint natively supports a 262,144-token context.

Environment Setup

pip install -U ms-swift
pip install -U "transformers>=5.16" "qwen_vl_utils>=0.0.14"

# Megatron
pip install -U mcore-bridge
pip install --no-build-isolation transformer_engine[pytorch]

# flash-linear-attention
pip install -U "flash-linear-attention>=0.5.2" --no-build-isolation

# causal_conv1d
pip install -U git+https://github.com/Dao-AILab/causal-conv1d --no-build-isolation

# The vllm build must include https://github.com/vllm-project/vllm/pull/53896
# For now, install from source (>0.28.0)
# See https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source

The training backend uses Megatron. For environment preparation, please refer to the Megatron-SWIFT Quick Start guide.

Fine-tuning (Megatron SFT)

Memory optimization: N-gram table offload

The N-gram embedding table holds 51B parameters (~95GiB in bf16), and can only be sharded along TP.

Set the PLE_CPU_OFFLOAD environment variable to offload the PLE weights:

PLE_CPU_OFFLOAD=1 megatron sft ...

Note that once enabled:

The table is frozen during training. LoRA training does not update it anyway, so there is no impact; under full-parameter training, however, these weights silently stop participating in training.

8-GPU LoRA fine-tuning:

# 8*70G
PLE_CPU_OFFLOAD=1 \
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
NPROC_PER_NODE=8 \
PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
megatron sft \
    --model Qwen/Qwen3.8-Flash-Next \
    --dataset 'swift/Chinese-Qwen3-235B-2507-Distill-data-110k-SFT#2000' \
    --num_train_epochs 1 \
    --tuner_type lora \
    --lora_rank 8 \
    --lora_alpha 32 \
    --target_modules in_proj out_proj linear_proj linear_qkv \
    --tensor_model_parallel_size 2 \
    --expert_model_parallel_size 4 \
    --expert_tensor_parallel_size 1 \
    --pipeline_model_parallel_size 2 \
    --decoder_first_pipeline_num_layers 12 \
    --sequence_parallel true \
    --moe_permute_fusion true \
    --recompute_granularity full \
    --recompute_method uniform \
    --recompute_num_layers 1 \
    --padding_free true \
    --max_length 8192 \
    --micro_batch_size 1 \
    --global_batch_size 8 \
    --lr 1e-4 \
    --save_steps 500 \
    --output_dir output

--decoder_first_pipeline_num_layers 12: the PLE layer (layer 2) lands on PP stage 0, so both its activations and its lookup cost fall on that stage; an even 24/24 split would therefore be unbalanced between the two stages. Assigning fewer layers to stage 0 evens it out.

Measured data

Measured on 8 GPUs with LoRA (rank 8), TP2/EP4/PP2 (except for the “parallelism” group), micro_batch_size=1, global_batch_size=8, on a synthetic dataset where every sample is exactly the same length. Memory is the per-GPU peak; step time is relative to the seq=2048 baseline = 1.00×.

Group	Config	seq	PF	RC	PLE offload	Mem (GiB)	Δ Mem	Time
baseline	seq=2048	2048	–	full	–	75.6	–	1.00×
sequence length	–	4096	–	full	–	77.8	+2.9%	1.03×
	–	8192	–	full	–	93.3	+23.4%	1.32×
memory switches	PLE offload	4096	–	full	✓	63.6	−15.9%	1.04×
	PLE offload	8192	–	full	✓	77.7	+2.8%	1.27×
	PLE offload + PF	8192	✓	full	✓	72.5	−4.1%	1.26×
	padding_free	4096	✓	full	–	77.8	+2.9%	1.16×
	recompute selective	4096	–	selective	–	92.1	+21.8%	0.75×
	recompute selective	8192	–	selective	–	123.5	+63.4%	0.87×
	recompute off	4096	–	none	–	92.1	+21.8%	0.74×
parallelism	TP4/EP2/PP2	8192	–	full	–	105.7	+39.8%	2.58×
	TP2/EP4/PP1	8192	–	full	–	142.5	+88.5%	0.80×

_{PF = padding_free, RC = recompute_granularity, PLE offload = PLE_CPU_OFFLOAD=1; “–” means off/default. The Time column is the relative single-step duration — higher is slower (e.g. 1.32× means each step takes 1.32× as long as the baseline).}

Reinforcement Learning (GRPO)

8-GPU GRPO LoRA training, with rollout using vLLM in colocate mode and max_completion_length of 8192:

# 8*135G
SYSTEM_PROMPT="Please reason step by step, and put your final answer within \\boxed{}."

PLE_CPU_OFFLOAD=1 \
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
NPROC_PER_NODE=8 \
PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
megatron rlhf \
    --rlhf_type grpo \
    --model Qwen/Qwen3.8-Flash-Next \
    --dataset open-r1/DAPO-Math-17k-Processed \
    --system "$SYSTEM_PROMPT" \
    --tuner_type lora \
    --lora_rank 8 \
    --lora_alpha 32 \
    --target_modules in_proj out_proj linear_proj linear_qkv \
    --tensor_model_parallel_size 2 \
    --expert_model_parallel_size 4 \
    --expert_tensor_parallel_size 1 \
    --pipeline_model_parallel_size 2 \
    --decoder_first_pipeline_num_layers 12 \
    --sequence_parallel true \
    --moe_permute_fusion true \
    --moe_aux_loss_coeff 1e-3 \
    --recompute_granularity full \
    --recompute_method uniform \
    --recompute_num_layers 1 \
    --padding_free true \
    --use_vllm true \
    --vllm_mode colocate \
    --vllm_enable_lora false \
    --vllm_gpu_memory_utilization 0.35 \
    --vllm_tensor_parallel_size 8 \
    --vllm_max_model_len 9216 \
    --sleep_level 2 \
    --offload_model true \
    --offload_optimizer true \
    --offload_bridge false \
    --num_train_epochs 1 \
    --global_batch_size 8 \
    --micro_batch_size 1 \
    --steps_per_generation 1 \
    --num_generations 4 \
    --reward_funcs accuracy \
    --max_length 1024 \
    --max_completion_length 8192 \
    --temperature 1.0 \
    --loss_type grpo \
    --beta 0.0 \
    --lr 5e-5 \
    --save_safetensors true \
    --merge_lora false \
    --logging_steps 1 \
    --log_completions true \
    --output_dir output