CN
重新定義 AI 推理基礎設施
GPNPU架構融合通用編程與專(zhuān)用效率，打造面向大模型推理時(shí)代的新一代AI推理芯片
構建面向下一代AI推理的
全新智算底座
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高性能計算'%3e%3cpath%20d='M20.6.6%201.2%2010.8l19.9%2010.5%2019.8-10.8L20.6.6Z'/%3e%3cpath%20d='M40.2%2014.6v16.6L20.7%2042.1V21.3M1.2%2013.7V32l17.2%209.5'/%3e%3c/g%3e%3cdefs%3e%3cclipPath%20id='a'%3e%3cpath%20fill='%23fff'%20d='M0%200h42.1v43.1H0z'/%3e%3c/clipPath%3e%3c/defs%3e%3c/svg%3e)
靈活編程'%3e%3cpath%20d='M18.9.9%201.1%2010.1c-.8.4-.8%201.6%200%202l17.8%208.4c.9.4%201.9.4%202.8%200l18.2-8.8c.8-.4.8-1.6%200-2L21.8.9c-.9-.5-2-.5-2.9%200ZM7.4%2023.7%201.1%2027c-.8.4-.8%201.6%200%202l17.8%208.4c.9.4%201.9.4%202.8%200l18.2-8.8c.8-.4.8-1.6%200-2l-7-3.4'/%3e%3cpath%20d='m7.4%2015.1-6.3%203.3c-.8.4-.8%201.6%200%202l17.8%208.4c.9.4%201.9.4%202.8%200L39.9%2020c.8-.4.8-1.6%200-2l-7-3.4'/%3e%3c/g%3e%3cdefs%3e%3cclipPath%20id='a'%3e%3cpath%20fill='%23fff'%20d='M0%200h41.1v38.2H0z'/%3e%3c/clipPath%3e%3c/defs%3e%3c/svg%3e)
高能效比'%3e%3cpath%20d='M27.1%2024c0%203.8-3.1%206.9-6.9%206.9-3.8%200-6.9-3.1-6.9-6.9%200-3.8%203.1-6.9%206.9-6.9%203.8%200%206.9%203.1%206.9%206.9ZM24.4%204.8c0%202.3-1.9%204.2-4.2%204.2C17.9%209%2016%207.1%2016%204.8%2016%202.5%2017.9.6%2020.2.6c2.3%200%204.2%201.8%204.2%204.2ZM9%2034.3c0%202.3-1.9%204.2-4.2%204.2-2.3%200-4.2-1.9-4.2-4.2%200-2.3%201.9-4.2%204.2-4.2%202.3%200%204.2%201.8%204.2%204.2Zm31.1%200c0%202.3-1.9%204.2-4.2%204.2-2.3%200-4.2-1.9-4.2-4.2%200-2.3%201.9-4.2%204.2-4.2%202.3%200%204.2%201.8%204.2%204.2Zm-25.8-6.4L8.5%2032m17.7-4.1L32%2032M20.2%209v8.1'/%3e%3c/g%3e%3cdefs%3e%3cclipPath%20id='a'%3e%3cpath%20fill='%23fff'%20d='M0%200h40.6v39H0z'/%3e%3c/clipPath%3e%3c/defs%3e%3c/svg%3e)
大模型優(yōu)化
Analysis
架構解析
.
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GPGPU
通用性
SIMT 通用編程能力
AI主流框架兼容，一行代碼實(shí)現程序運行
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NPU
高效性
SIMD 矩陣計算密度
NPU級別的計算效率和能效比
近存計算
大帶寬、低延時(shí)
3D堆疊架構
高效訪(fǎng)存架構
10ns級別訪(fǎng)存延遲
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算力積木
超節點(diǎn)
Scale up 算力擴展
滿(mǎn)足10萬(wàn)億級別的 MoE大模型推理需要
通用性
統一線(xiàn)程執行，真正承接主流開(kāi)發(fā)生態(tài)
以SIMT通用執行模型為底座，兼容AI主流框架與軟件生態(tài)，支持復雜控制流、動(dòng)態(tài)調度與完整
推理流程承載。
高效性
通用架構也能實(shí)現 NPU 級效率
通過(guò) SIMD、張量融合計算、混合精度執行與片上算存協(xié)同，提升有效算力、吞吐表現與單位功耗產(chǎn)出。
大寬帶
以3D堆疊重構數據供給架構
通過(guò)3D堆疊存儲與分層帶寬池化，提升數據帶寬與訪(fǎng)問(wèn)效率，支撐長(cháng)上下文與MoE等高數據吞吐場(chǎng)景。
低延時(shí)
面向實(shí)時(shí)智能的響應式架構
依托近核熱數據駐留、低跳數互連與解碼優(yōu)先調度，顯著(zhù)改善TTFT、TPOT與尾時(shí)延表現。
超節點(diǎn)
從單芯片走向超節點(diǎn)推理平臺
通過(guò)統一內存語(yǔ)義的高速互連與系統級擴展架構，支撐從單卡、整機到超節點(diǎn)的持續擴展。
DOMESTIC TECHNOLOGY
IFWA軟件棧
