Model overview
*Weight-only NVFP4 quantization of Z.ai's GLM-5.3-Flash — the first GLM to combine **sparse and linear attention**.* A **weight-only NVFP4 (NVFP4-A16)** checkpoint. The **routed-expert FFN** tensors — 97% of the model's parameters — are quantized to **NVFP4** (E2M1, with FP8-E4M3 per-16-block scales and an FP32 per-tensor global scale). Everything outlier-sensitive stays in **BF16**: > both attention flavours (all 34 KDA linear-attention layers *and* the 11 DeepSeek-sparse layers, including the sparse indexer) · the **entire vision tower** · shared experts · MoE routers · dense-MLP layers · the MTP head · the Manifold-Constrained Hyper-Connection (mHC) tensors · token embeddings · `lm_head` · all norms
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。