螞蟻集團百靈大模型系列更新，Ling-2.6-flash正式開放。該模型總參數104B，激活參數7.4B，提供BF16、FP8、INT4等多精度版本，適配不同硬件環境並降低部署門檻。此前以“Elephant Alpha”匿名在OpenRouter平臺測試。
國產AI芯片與大模型協同優化取得重要進展。摩爾線程與硅基流動基於國產GPU MTT S5000，成功完成對千億參數大模型DeepSeek V3 671B的深度適配。通過應用FP8低精度推理技術，實現單卡預填充吞吐量超4000 tokens/秒，解碼吞吐量超1000 tokens/秒，推理速度已接近國際主流高端AI加速器水平。
螞蟻集團開源萬億參數大模型Ling-1T，採用FP8低精度訓練，爲當前最大基座模型。該模型由"百靈"團隊開發，屬於Ling2.0家族，包含Ling、Ring、Ming三大系列。其中Ling系列專注通用任務，強調速度與效率優勢。
智譜發佈GLM-4.6模型，採用寒武紀國產芯片，首次實現FP8+Int4混合量化部署。該技術突破在保持模型精度的同時顯著降低推理成本，爲國產芯片支持大模型本地化運行開闢新路徑。
Google
$0.49
輸入tokens/百萬
$2.1
輸出tokens/百萬
1k
上下文長度
Openai
$2.8
$11.2
Xai
$1.4
$3.5
2k
$7.7
$30.8
200
-
Anthropic
$105
$525
$0.7
$7
$35
$17.5
$21
Alibaba
$6
$24
256
$4
$16
Baidu
128
Bytedance
$1.2
$3.6
4
$2
Moonshot
$0.8
kyr0
這是一個專為蘋果硅芯片設備優化的自動語音識別模型，通過轉換為MLX框架並量化為FP8格式，實現在蘋果設備上的快速端上語音轉錄。該模型針對逐字精度進行微調，特別適用於需要高精度轉錄的場景。
ibm-granite
Granite 4.0 H-Small (FP8) 是IBM開發的Granite系列語言模型的小型FP8量化版本，專為提供專業、準確和安全的回覆而設計。該模型採用FP8精度格式，在保持性能的同時優化了推理效率。
nvidia
NVIDIA Qwen3-235B-A22B FP8模型是阿里雲Qwen3-235B-A22B模型的量化版本，採用優化的Transformer架構，是一個自迴歸語言模型。該模型通過FP8量化技術顯著減少了磁盤空間和GPU內存需求，同時保持較高的推理精度，適用於各種AI應用場景。
nm-testing
QwQ-32B的FP8量化版本，通過動態量化技術減少50%存儲和內存需求，同時保持99.75%的原模型精度
RedHatAI
QwQ-32B的FP8量化版本，通過動態量化技術減少50%存儲和內存需求，同時保持99.75%的原始模型精度
lllyasviel
Omost 的 phi-3-mini 模型，具有 128k 上下文長度，採用 fp8 精度。
FriendliAI
由FriendliAI量化為FP8精度的Mistral-7B-Instruct-v0.2模型，在保持高精度的同時顯著提升推理效率。
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
'%3e%3cpath%20d='M55.2622%2038L48.2289%2022L44.1206%2031.3333H47.2539L45.7872%2034.6667H38.7622L46.0956%2018H37.6706C36.7539%2018%2036.0039%2018.75%2036.0039%2019.6667V36.3333C36.0039%2037.25%2036.7539%2038%2037.6706%2038H55.2622Z'%20fill='white'/%3e%3cpath%20d='M64.3368%2018H50.3535L59.1535%2038H64.3368C65.2535%2038%2066.0035%2037.25%2066.0035%2036.3333V19.6667C66.0035%2018.75%2065.2535%2018%2064.3368%2018ZM63.0868%2034.6667H59.7535V21.3333H63.0868V34.6667Z'%20fill='%23FFCC66'/%3e%3cpath%20d='M89.3379%2026.75V29.25H81.8379V36.75H91.8379V24.25H81.8379V26.75H89.3379ZM84.3379%2034.25V31.75H89.3379V34.25H84.3379Z'%20fill='white'/%3e%3cpath%20d='M103.504%2036.75V29.25H96.0039V26.75H103.504V24.25H93.5039V31.75H101.004V34.25H93.5039V36.75H103.504Z'%20fill='white'/%3e%3cpath%20d='M115.17%2026.75V24.25H105.17V36.75H115.17V34.25H107.67V31.75H115.17V29.25H107.67V26.75H115.17Z'%20fill='white'/%3e%3cpath%20d='M80.1712%2029.25V29.175L78.9962%2028L80.1712%2026.825V20.425L78.9962%2019.25H69.3379V36.75H78.9962L80.1712%2035.575V29.25ZM77.6712%2034.25H71.8379V29.25H77.6712V34.25ZM77.6712%2026.75H71.8379V21.75H77.6712V26.75Z'%20fill='white'/%3e%3c/g%3e%3cg%20clip-path='url(%23clip1_644_3948)'%3e%3cpath%20d='M169.59%2022.9827L169.59%2023.6491H177.409L168.175%2032.8822L169.118%2033.8248L178.351%2024.5917L178.352%2032.4106H179.685V22.3164L179.018%2022.3157H169.59V22.9827Z'%20fill='white'/%3e%3c/g%3e%3cdefs%3e%3cclipPath%20id='clip0_644_3948'%3e%3crect%20width='80'%20height='20'%20fill='white'%20transform='translate(36%2018)'/%3e%3c/clipPath%3e%3cclipPath%20id='clip1_644_3948'%3e%3crect%20width='20'%20height='20'%20fill='white'%20transform='translate(164%2018)'/%3e%3c/clipPath%3e%3c/defs%3e%3c/svg%3e)
