GLM-5.3-Flash-DFlash2-MXFP8

This repository contains an MXFP8-quantized DFlash 2 draft model for local-inference-lab/GLM-5.3-Flash-NVFP4. It is not a standalone language model. A compatible speculative-decoding server loads it beside the target model and verifies every drafted token against the target.

The source checkpoint is incoai/GLM-5.3-Flash-DFlash2 at immutable revision dc77ff1c99eeb2df044ee3d4f0094eb033fee410.

Format

• Linear weights: float8_e4m3fn
• Scale values: biased E8M0 exponents stored as uint8
• Quantization block: 1×32 values
• Scale layout: row-major and unswizzled
• Excluded module: lm_head
• Draft KV cache quantization: not encoded in the checkpoint

conversion_manifest.json records the immutable source revision, source and output checksums, tensor coverage, aggregate quantization error, and per-weight validation statistics.

Validation status

Status: qualified for checkpoint structure, exact format reproduction, loading, and smoke inference under the following conditions:

• Target: local-inference-lab/GLM-5.3-Flash-NVFP4 revision 520de24eabf507659eaef7c70f14fd584527facc
• Runtime: voipmonitor/vllm:glm53-flash-nvfp4-dflash2-community-20260830-r5
• Hardware: four NVIDIA RTX PRO 6000 Blackwell Workstation Edition GPUs
• Parallelism: tensor parallel size 4 and decode-context parallel size 1
• Target attention, MoE, linear, and tensor-parallel all-reduce: B12X
• DFlash attention: FlashAttention 2
• DFlash linear: B12X MXFP8
• DFlash proposal length: seven tokens
• DFlash KV cache: auto (BF16)
• CUDA graph mode: NONE for the load-and-inference qualification

The runtime detected ModelOpt MXFP8, selected B12xMxfp8LinearKernel for draft GEMMs and the fused DFlash context K/V projection, loaded 1.20 GB of draft weights, and completed a 32-token speculative request. Runtime throughput and CUDA graph performance are not qualified by that smoke test.

The checkpoint is unsupported in vLLM builds that do not contain the DFlash 2 and ModelOpt MXFP8 integration used by the qualified runtime.

Serving

docker run --rm \
  --gpus '"device=0,1,2,3"' \
  --network host \
  --ipc host \
  --shm-size 32g \
  -e MODEL=local-inference-lab/GLM-5.3-Flash-NVFP4 \
  -e SERVED_MODEL_NAME=GLM-5.3-Flash-NVFP4 \
  -e PORT=8000 \
  -e TP=4 \
  -e DCP=1 \
  -e MAX_NUM_SEQS=16 \
  -e MAX_MODEL_LEN=262144 \
  -e MAX_NUM_BATCHED_TOKENS=4096 \
  -e SPECULATOR=dflash \
  -e NUM_SPECULATIVE_TOKENS=7 \
  -e DFLASH_MODEL=local-inference-lab/GLM-5.3-Flash-DFlash2-MXFP8 \
  -e DFLASH_MODEL_REVISION= \
  -e DFLASH_KV_CACHE_DTYPE=auto \
  -e DFLASH_ATTENTION_BACKEND=FLASH_ATTN \
  -e ATTENTION_BACKEND=B12X \
  -e MOE_BACKEND=b12x \
  -e LINEAR_BACKEND=b12x \
  -e B12X_PCIE_ALLREDUCE=1 \
  -e CUDAGRAPH_MODE=FULL \
  -e VLLM_B12X_MOE_FP4_FORCE_A16=0 \
  voipmonitor/vllm:glm53-flash-nvfp4-dflash2-community-20260830-r5

An empty DFLASH_MODEL_REVISION makes the launcher resolve the repository's main branch. For reproducible deployments, replace the empty value with an immutable Hugging Face commit hash. The OpenAI-compatible endpoint is available at http://127.0.0.1:8000/v1.

License and attribution

The source DFlash 2 model is distributed under CC BY-NC-ND 4.0. See the source model card for its use restrictions and attribution information.

@misc{inco2026dflash2,
  title  = {{DFlash 2: Keep Drafting Parallel}},
  author = {{Inco AI}},
  year   = {2026},
  month  = {August},
  url    = {https://inco.ai/blog/dflash2/}
}