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AI 模型库/Jab1718/qwen3.8-flash-coder-44gb-selective-int8
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qwen3.8-flash-coder-44gb-selective-int8

Jab1718 · qwen3.8-flash-coder-44gb-selective-int8

**`Qwen3.8-Flash-Coder-44GB-Selective-INT8`** is a high-performance, selective-quantized Mixture-of-Experts (MoE) coding model. Sliced down from the monolithic **`Qwen/Qwen3.8-Flash-Next` (335GB)** and quantized from the **`Qwen3.8-Flash-Coder-85GB-BF16`** parent checkpoint, this model reduces disk and VRAM footprint to exactly **44.29 GB** (a **44.2% VRAM reduction** and **86.8% reduction from base**), enabling full zero-offload deployment on **only 2x 32GB GPUs** (e.g. 2x NVIDIA RTX 5000 Ada, 2x RTX 4090/3090, or 1x A100/H100 80GB). Traditional MoE post-training quantization often quantizes all layers uniformly, which severely degrades the Router Gate and causes **Routing Collapse** (routing tokens to sub-optimal experts). This checkpoint introduces **Selective MoE Quantization**: 1. **Critical High-Precision Modules (Kept in 100% Native BF16):** - **Router Gates:** Retain 100% floating-point routing fidelity across all 48 layers. - **Multi-Head Self-Attention & Linear Attention:** `q_proj`, `k_proj`, `v_proj`, `o_proj`. - **Shared Expert, RMSNorms, Embeddings & LM Head:** Zero quantization loss in embedding projections. 2. **High-Capacity Sparse Experts (Quantized to Symmetric Per-Channel INT8):** - 160 MoE Experts across 48 layers (`gate_up_proj`, `down_proj`). - Symmetrically quantized per-channel with dynamic scaling vectors (`gate_up_proj_scale`, `down_proj_scale`).

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档案更新时间2026/11/2 19:17:06
OVERVIEW

模型介绍

**`Qwen3.8-Flash-Coder-44GB-Selective-INT8`** is a high-performance, selective-quantized Mixture-of-Experts (MoE) coding model. Sliced down from the monolithic **`Qwen/Qwen3.8-Flash-Next` (335GB)** and quantized from the **`Qwen3.8-Flash-Coder-85GB-BF16`** parent checkpoint, this model reduces disk and VRAM footprint to exactly **44.29 GB** (a **44.2% VRAM reduction** and **86.8% reduction from base**), enabling full zero-offload deployment on **only 2x 32GB GPUs** (e.g. 2x NVIDIA RTX 5000 Ada, 2x RTX 4090/3090, or 1x A100/H100 80GB). Traditional MoE post-training quantization often quantizes all layers uniformly, which severely degrades the Router Gate and causes **Routing Collapse** (routing tokens to sub-optimal experts). This checkpoint introduces **Selective MoE Quantization**: 1. **Critical High-Precision Modules (Kept in 100% Native BF16):** - **Router Gates:** Retain 100% floating-point routing fidelity across all 48 layers. - **Multi-Head Self-Attention & Linear Attention:** `q_proj`, `k_proj`, `v_proj`, `o_proj`. - **Shared Expert, RMSNorms, Embeddings & LM Head:** Zero quantization loss in embedding projections. 2. **High-Capacity Sparse Experts (Quantized to Symmetric Per-Channel INT8):** - 160 MoE Experts across 48 layers (`gate_up_proj`, `down_proj`). - Symmetrically quantized per-channel with dynamic scaling vectors (`gate_up_proj_scale`, `down_proj_scale`).

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
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

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。

查看完整已收录榜单 →

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

huggingface · Jab1718/qwen3.8-flash-coder-44gb-selective-int8 ↗2026/10/30 05:07:43
huggingface · enrich:3542692e-cc93-42dc-9d99-0546b58edf64 ↗2026/10/30 19:13:19

许可证：apache-2.0。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。