Can Attention-FFN Disaggregation still win on the newest rack-scale GPU systems? We built FastAFD, an open-source AFD runtime for GB200 NVL72: 72 Blackwell GPUs in one NVLink domain. It improves per-GPU decode throughput by 1.35-1.45×. Code: https://github.com/hao-ai-lab/FastAFD… Blog: https://haoailab.com/blogs/fastafd/

hao-ai-lab/FastAFD

Source: https://github.com/hao-ai-lab/FastAFD

Fast Attention–FFN Disaggregation for MoE Serving

| Blog | X |

FastAFD is an open-source serving system for large-scale Attention–FFN Disaggregation (AFD) of MoE models.

News

• [2026-07-07] FastAFD is open-sourced — 1.3–1.5× per-GPU decode throughput over colocated MoE serving on Blackwell NVL72.

Contents

• How FastAFD Works
• Key Techniques
  • N2M/M2N MoE Megakernel
  • Micro-Batch Ping-Pong Overlap
  • Zero-Overhead Cluster Coordination
• Results
• Getting Started
  • Install
  • Supported Models
  • Quickstart
  • Correctness Alignment
  • Large-Scale AFD Experiments
• Acknowledgements
• Citation

How FastAFD Works

FastAFD splits MoE decoding into attention servers and MLP servers. Attention servers own KV-cache attention, routing, and sampling; MLP servers aggregate routed hidden states from many attention servers and run the experts. Micro-batches overlap dispatch, expert execution, and combine so the MLP side stays dense while activations move.

Key Techniques

N2M/M2N MoE Megakernel

FastAFD maps the AFD boundary to an asymmetric DeepEP-style dispatch/combine path. During N2M, attention ranks send routed activations and MLP ranks receive expert inputs; during M2N, MLP ranks return combined outputs and attention ranks receive layer results. On the MLP side, FastAFD runs the repeated receive, group, expert, combine, and send-back loop as a role-specialized MoE path with fewer launches and synchronization points.

Micro-Batch Ping-Pong Overlap

FastAFD splits each decode batch into micro-batches so N2M dispatch, expert execution, M2N return, and attention-side work overlap across layers instead of serializing at every AFD boundary.

Zero-Overhead Cluster Coordination

FastAFD prepares the next decode plan while GPUs execute the current one. The coordinator publishes micro-batch order, buffer slots, request ownership, and peer metadata one step ahead, so scheduling stays off the decode critical path.

Results

FastAFD improves per-GPU decode throughput by 1.35–1.45× over colocated MoE serving across Qwen3-235B-A22B-FP8 and MiniMax-M2.5 on GB200 NVL72.

Getting Started

FastAFD is currently validated on aarch64 with CUDA 13.0. The conda environment is named minisgl-cuda130 for compatibility with the current runtime scripts.

Install

conda env create -f environment.cuda130.yml
./scripts/enter_clean.sh minisgl-cuda130
pip install -e .

Install vLLM in the same clean shell if you want to run alignment checks:

python -m pip install \
  "https://github.com/vllm-project/vllm/releases/download/v0.19.0/vllm-0.19.0+cu130-cp38-abi3-manylinux_2_35_aarch64.whl" \
  --extra-index-url https://download.pytorch.org/whl/cu130

Then apply the runtime overrides used by the AFD/DeepEP path:

python -m pip install --no-cache-dir --force-reinstall --no-deps \
  "nvidia-nccl-cu13==2.30.4" \
  "setuptools==80.10.2" \
  "fsspec==2026.2.0"

Validated package versions are torch 2.10.0+cu130, triton 3.6.0, nvidia-nccl-cu13 2.30.4, and vllm 0.19.0+cu130 when alignment is enabled.

Supported Models

• Quickstart and correctness: Qwen/Qwen3-30B-A3B-Instruct-2507
• Published scaling presets: Qwen3-235B-A22B-FP8 and MiniMax-M2.5-FP8

Quickstart

./scripts/quickstart/qwen3_30b_a3b_sample.sh

This starts a local mini-sgl server, samples the Qwen3-30B-A3B prompt set, and writes artifacts under reports/.

Correctness Alignment

./scripts/validate/qwen3_30b_a3b_alignment.sh
./scripts/validate/qwen3_30b_a3b_fastafd_alignment.sh

The first script checks mini-sgl against vLLM (single node, 4 GPUs via the mp backend). The second checks the FastAFD serve path against vLLM and needs a running Ray cluster with at least 8 GPUs (attention TP 4 + MLP TP 4).

Both alignment commands are thin 30B presets that call one reusable validator, scripts/validate/fastafd_vllm_alignment.sh, which aligns the FastAFD serve path against vLLM for any model or AFD attention/MLP layout. To validate a different model or a custom split, call it directly (run with no arguments for the full option list):

./scripts/validate/fastafd_vllm_alignment.sh \
  --env minisgl-cuda130 --model <hf-id-or-path> \
  --prompt-file <utf8-prompts.txt> \
  --attn-tp-size 4 --mlp-tp-size 4 --afd-mlp-ep-size 4 \
  --afd-max-running-requests 64 --sample-concurrency 64 \
  --max-new-tokens 64

Large-Scale AFD Experiments

Presets live under scripts/experiments/afd/ and assume a running Ray GPU cluster, launched from the activated minisgl-cuda130 environment. Nodes are auto-discovered from RAY_ADDRESS=auto (last node = MLP, the rest = attention); on multi-node clusters use a shared local MODEL_PATH snapshot to avoid per-rank Hugging Face cache resolution.

The 512-prompt long-context caches ship under prompts/. Regenerate them only if needed (the 16K cache uses --target-tokens 16384 and adds --min-source-tokens 20000):

PYTHONPATH=python python scripts/data_gen/generate_realistic_long_prompts.py \
  --model Qwen/Qwen3-235B-A22B-FP8 --target-tokens 8192 --count 512 \
  --output prompts/prompts_512x8192_seed20260527.txt --seed 20260527

Run a preset (minimal 4-node, no vLLM scoring or Nsight profiling):

MODEL_PATH=/path/to/Qwen3-235B-A22B-FP8 AFD_TOTAL_NODES=4 \
RUN_VLLM_ALIGNMENT=0 NSYS=0 \
bash scripts/experiments/afd/qwen3_235b/run_afd_qwen3_235b_a22b_fp8_8k_b96_dynamicnode_mb2_nsys_alignment.sh

Optional environment knobs:

Available presets:

vLLM cross-check (RUN_VLLM_ALIGNMENT=1). Scores through an expert-parallel vLLM baseline (--all2all-backend deepep_low_latency --moe-backend deep_gemm), which needs two optional kernel packages in the same environment: deep_ep (all-to-all dispatch/combine) and deep_gemm (FP8 grouped-GEMM experts — vLLM only enables its BATCHED_DEEPGEMM MoE backend when import deep_gemm succeeds, otherwise the engine fails to start). Install both via vLLM’s tools/ep_kernels, or build deep_gemm from source (pip install --no-deps . in a DeepGEMM clone). Both are independent of the FastAFD serve path, which uses its own vendored kernels. On memory-tight nodes, lower --gpu-memory-utilization (e.g. to 0.85) via VLLM_EXTRA_ARGS to avoid OOM during CUDA-graph capture.

Each preset prints the selected topology and output directory at startup. See scripts/README.md for the full script map.

Acknowledgements

FastAFD started from a mini-sglang serving codebase inspired by SGLang, and builds on ideas and components from the broader open-source LLM systems community, including DeepEP, DeepGEMM, vLLM.

Citation

If you find FastAFD useful, please cite:

@misc{fastafd2026,
  title  = {FastAFD: Open-Source Large-Scale Attention-FFN Disaggregation on Blackwell NVL72},
  author = {Fu, Yichao and Zhang, Yuxuan and Wang, Ruitian and Chen, Junda and Zhang, Hao},
  year   = {2026},
  url    = {https://github.com/hao-ai-lab/FastAFD},
  note   = {Technical blog and open-source release}
}