MODEL PROFILE · 模型档案

GLM-5.3-Flash-NVFP4

tattrongvu · GLM-5.3-Flash-NVFP4

NVFP4 (4-bit float, group size 16) quantization of `zai-org/GLM-5.3-Flash`, covering the routed experts of **every** MoE layer — including layer 45, the multi-token-prediction (MTP) layer — in a **single, uniform** scheme. **No calibration data is used in this quantization step**, and none is needed: the scheme quantizes each group of 16 weights with a scale derived from those 16 values (`memoryless_minmax`), so there is no forward pass for calibration data to inform. The checkpoint contains no activation-scale tensors and declares `input_activations: null`. `num_hidden_layers: 45` plus `num_nextn_predict_layers: 1` puts the MTP layer at index **45**, and it is not a small head: 288 routed experts, ~7.25 B parameters, its own DSA attention and `eh_proj`/`enorm`/`hnorm` plumbing. Keeping it in the same scheme as every other expert layer means the checkpoint has one expert layout throughout — 37,152 packed expert tensors (43 × 288 × 3).

译文排队中,当前展示来源原文。

视觉问答license:mit
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:35
OVERVIEW

模型介绍

NVFP4 (4-bit float, group size 16) quantization of `zai-org/GLM-5.3-Flash`, covering the routed experts of **every** MoE layer — including layer 45, the multi-token-prediction (MTP) layer — in a **single, uniform** scheme. **No calibration data is used in this quantization step**, and none is needed: the scheme quantizes each group of 16 weights with a scale derived from those 16 values (`memoryless_minmax`), so there is no forward pass for calibration data to inform. The checkpoint contains no activation-scale tensors and declares `input_activations: null`. `num_hidden_layers: 45` plus `num_nextn_predict_layers: 1` puts the MTP layer at index **45**, and it is not a small head: 288 routed experts, ~7.25 B parameters, its own DSA attention and `eh_proj`/`enorm`/`hnorm` plumbing. Keeping it in the same scheme as every other expert layer means the checkpoint has one expert layout throughout — 37,152 packed expert tensors (43 × 288 × 3).

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
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按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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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常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

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。