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AIモデルライブラリ/peasantsmith/GLM-5.3-Flash-Maya-GGUF
モデル情報

GLM-5.3-Flash-Maya-GGUF

peasantsmith · GLM-5.3-Flash-Maya-GGUF

**Maya-L: 99.2% of the full FP8 model's accuracy** on zero-shot tasks (ARC, HellaSwag, WinoGrande, PIQA), in 156 GB. **Maya-S: 97.9%** in a 96 GB file. **Maya-S24: 97.7%, and up to 14% faster decode on 24 GB cards**, in 94.7 GB. **Maya-M: 97.9%, and closer to the full model token by token**, in 116 GB. GGUF quantizations of zai-org/GLM-5.3-Flash (321 B parameters, a mixture of experts with about 18 B active per token), made for **Project Maya**. Project Maya keeps the most-used experts on the GPU, the next in RAM and the rest on the SSD, so all four run well below the model's size (two GPUs with 30 GB of RAM in the measurements below). More memory is faster, and every machine is different: try it on yours. All four are made from Z.ai's own FP8 release - the precision the model is served at - not from a re-quantized file. - **Maya-S** (96.5 GB) is the compact one, made for PCs with a smaller memory pool across RAM and VRAM: its routed experts - most of the model - in about 2 bits (IQ2_XXS / IQ2_S), its attention and shared experts in 6-bit (Q6_K). - **Maya-S24** (94.7 GB) is Maya-S for 24 GB cards (RTX 3090 / 4090): the same 2-bit routed experts, with only the small part every token runs through - the attention and shared experts - in 4-bit (Q4_K) instead of Maya-S's 6-bit. That leaves about 1.5 GB more room for experts on the GPU. - **Maya-M** (116 GB) is closer to the full model, for PCs with a bigger memory pool: built with the same FP8-statistics recipe and more bits where they count (2- to 3-bit experts), calibrated toward tool calls and front-end code. - **Maya-L** (156.3 GB) is the closest, for PCs with the biggest memory pool: Maya-M's recipe one step up (3- to 4-bit experts: IQ3_S / IQ4_XS, Q5_K in the most sensitive layers).

翻訳待ちのため原文を表示しています。

テキスト生成license:mit
コンテキスト長非公開
入力 → 出力テキスト → テキスト
モデルウェイト未確認
情報更新日時2026/11/2 19:16:49
OVERVIEW

モデル紹介

**Maya-L: 99.2% of the full FP8 model's accuracy** on zero-shot tasks (ARC, HellaSwag, WinoGrande, PIQA), in 156 GB. **Maya-S: 97.9%** in a 96 GB file. **Maya-S24: 97.7%, and up to 14% faster decode on 24 GB cards**, in 94.7 GB. **Maya-M: 97.9%, and closer to the full model token by token**, in 116 GB. GGUF quantizations of zai-org/GLM-5.3-Flash (321 B parameters, a mixture of experts with about 18 B active per token), made for **Project Maya**. Project Maya keeps the most-used experts on the GPU, the next in RAM and the rest on the SSD, so all four run well below the model's size (two GPUs with 30 GB of RAM in the measurements below). More memory is faster, and every machine is different: try it on yours. All four are made from Z.ai's own FP8 release - the precision the model is served at - not from a re-quantized file. - **Maya-S** (96.5 GB) is the compact one, made for PCs with a smaller memory pool across RAM and VRAM: its routed experts - most of the model - in about 2 bits (IQ2_XXS / IQ2_S), its attention and shared experts in 6-bit (Q6_K). - **Maya-S24** (94.7 GB) is Maya-S for 24 GB cards (RTX 3090 / 4090): the same 2-bit routed experts, with only the small part every token runs through - the attention and shared experts - in 4-bit (Q4_K) instead of Maya-S's 6-bit. That leaves about 1.5 GB more room for experts on the GPU. - **Maya-M** (116 GB) is closer to the full model, for PCs with a bigger memory pool: built with the same FP8-statistics recipe and more bits where they count (2- to 3-bit experts), calibrated toward tool calls and front-end code. - **Maya-L** (156.3 GB) is the closest, for PCs with the biggest memory pool: Maya-M's recipe one step up (3- to 4-bit experts: IQ3_S / IQ4_XS, Q5_K in the most sensitive layers).

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

—

試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

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。

収録済みの全ランキング →

よくある質問

オープンウェイトなら無料で商用利用できますか？

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか？

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

huggingface · peasantsmith/GLM-5.3-Flash-Maya-GGUF ↗2026/10/28 23:23:13
huggingface · enrich:b325f092-c601-4ea9-86f8-d8142e73582c ↗2026/10/28 23:03:28

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。