This checkpoint preserves the GLM-5.1 MoE + MLA + DSA architecture from the BF16 source, with all Linear weights quantized to FP8 E4M3 for ~2x compression.

Quantization Strategy

Per-channel FP8 E4M3 weight quantization with dynamic per-token activation scaling:

Precision	Layers
FP8 E4M3	All Linear weights: MLA projections, MLP gate/up/down, expert projections, DSA indexer
BF16	`lm_head`, `embed_tokens`, MoE router gates, norms

Architecture match with the BF16 source:

model_type=glm_moe_dsa
78 layers (3 dense + 75 MoE, first_k_dense_replace=3)
n_routed_experts=256, num_experts_per_tok=8, n_shared_experts=1
max_position_embeddings=202752
hidden_size=6144, moe_intermediate_size=2048
vocab_size=154880

Calibration

512 self-calibration samples generated from GLM-5.1 via OpenRouter (top-tier provider routing)
8 diverse categories: math, code, logic, analysis, creative writing, general knowledge, agentic/tool-calling, Korean
Activation statistics collected layer-by-layer for per-channel FP8 scale computation

Usage

SGLang

bash

python3 -m sglang.launch_server --model mconcat/GLM-5.1-FP8-Dynamic \
  --tensor-parallel-size 8 \
  --dtype bfloat16 \
  --trust-remote-code \
  --mem-fraction-static 0.80

vLLM

bash

vllm serve mconcat/GLM-5.1-FP8-Dynamic \
  --tensor-parallel-size 8 \
  --dtype bfloat16 \
  --trust-remote-code

Compatibility

Framework	Supported	Notes
vLLM >= 0.19.0	Yes	Requires `glm_moe_dsa` + compressed-tensors support
SGLang >= 0.5.10	Yes	Requires GLM-5.1 architecture support
transformers >= 5.4.0	Yes	Direct loading with `device_map="auto"`

Notes

This is a 754B MoE model (~40B active per token). Requires multi-GPU setup for inference (8x 80GB+ GPUs recommended).
FP8 E4M3 provides ~2x compression over BF16 with minimal quality degradation.
Compatible with Hopper (SM90) and Blackwell GPUs.
Dynamic activation scaling — scales computed at inference time, not baked into the checkpoint.
GLM-5.1 does not ship MTP weights despite num_nextn_predict_layers=1 in config.

Blackwell SM120 Patch (RTX PRO 6000 / Workstation GPUs)

If running on Blackwell workstation GPUs (SM 12.0), vLLM 0.19.0 requires patches for FlashMLA sparse attention support:

bash

# Patch 1: FlashMLA ops - add SM120 to sparse support check
FLASHMLA_OPS=$(python -c "import vllm, os; print(os.path.join(os.path.dirname(vllm.__file__), 'v1/attention/ops/flashmla.py'))") && \
sed -i 's/is_device_capability_family(90)\s*or current_platform.is_device_capability_family(100)/is_device_capability_family(90) or current_platform.is_device_capability_family(100) or current_platform.is_device_capability_family(120)/' "$FLASHMLA_OPS"

# Patch 2: FlashMLA sparse backend - add SM12 to capability check
FLASHMLA_SPARSE=$(python -c "import vllm, os; print(os.path.join(os.path.dirname(vllm.__file__), 'v1/attention/backends/mla/flashmla_sparse.py'))") && \
sed -i 's/return capability.major in \[9, 10\]/return capability.major in [9, 10, 12]/' "$FLASHMLA_SPARSE"

# Patch 3: FlashMLA dense backend (if exists)
FLASHMLA_DENSE=$(python -c "import vllm, os; print(os.path.join(os.path.dirname(vllm.__file__), 'v1/attention/backends/mla/flashmla.py'))") && \
sed -i 's/return capability.major in \[9, 10\]/return capability.major in [9, 10, 12]/' "$FLASHMLA_DENSE" 2>/dev/null || true

These patches add SM120 (Blackwell workstation) to the supported compute capability list for GLM-5.1's DSA sparse attention.

Quantization Process

Tool: Custom layer-by-layer pipeline with native torch.float8_e4m3fn dtype
Hardware: Single NVIDIA RTX PRO 6000 Blackwell (96 GB), processed one layer at a time
Time: ~319 minutes for 78 layers
Calibration: 256 samples, per-module activation statistics with MoE expert input hooks