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AI ASIC ½ÃÀåÀº ºü¸£°Ô ¼ºÀåÇϰí ÀÖÀ¸¸ç, TPU´Â ¿¡³ÊÁö È¿À²¼º°ú ¸ÂÃãÇüÀ¸·Î ÀÎÇØ »ç³» »ç¿ë¿¡¼ ¿ÜºÎ Á¦Ç°È·Î ÀüȯÇϰí ÀÖ½À´Ï´Ù. ÁÖ¿ä Ŭ¶ó¿ìµå ¹× ÇÏÀÌÅ×Å© ±â¾÷µéÀº ºñ¿ë°ú °ø±Þ ¸®½ºÅ©¸¦ ÁÙÀ̱â À§ÇØ ÀÚü ASIC¸¦ Àû±ØÀûÀ¸·Î °³¹ßÇϰí ÀÖÀ¸¸ç, AI Çϵå¿þ¾î¸¦ °í¼º´É, ÀúÀü·Â, ´Ù¾çÇÑ ¾ÖÇø®ÄÉÀ̼ÇÀ» À§ÇÑ °í¼º´É, ÀúÀü·Â, ´Ù¾çÇÑ ¾ÖÇø®ÄÉÀ̼ÇÀ¸·Î ¹ßÀü½ÃÄÑ GPU¿¡ ÀÌ¾î µÎ ¹øÂ°·Î Å« °¡¼Ó±â ¿ªÇÒÀ» Çϰí ÀÖ½À´Ï´Ù.
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- AI ASIC ½ÃÀåÀº Ŭ¶ó¿ìµå ¼ºñ½º ¼ö¿ä Áõ°¡¿Í AI ¸ðµ¨ ±Ô¸ð È®´ë¿¡ µû¶ó ºü¸£°Ô ¼ºÀåÇϰí ÀÖ½À´Ï´Ù.
- TPU´Â ¿¡³ÊÁö È¿À²°ú ¼öÁ÷ ÅëÇÕ¿¡¼ ¿ìÀ§¸¦ Á¡Çϰí, ÇÏÀÌ¿£µå GPUº¸´Ù ¿ì¼öÇÑ ºñ¿ë ±¸Á¶¿Í »ç¿ëÀÚ Á¤ÀÇ °¡´É¼ºÀ» Á¦°øÇÕ´Ï´Ù.
- ¹Ì±¹ÀÇ ÁÖ¿ä Ŭ¶ó¿ìµå Á¦°ø¾÷ü¿Í ÁÖ¿ä ÇÏÀÌÅ×Å© ±â¾÷(Tesla, OpenAI, Apple)Àº ƯÁ¤ AI ¾ÖÇø®ÄÉÀ̼ǿ¡ ¸Â´Â ÀÚü ASICÀ» Àû±ØÀûÀ¸·Î °³¹ßÇϰí ÀÖ½À´Ï´Ù.
- ÀÚü ASIC °³¹ßÀº Àå±âÀûÀÎ °è»ê ºñ¿ë°ú °ø±Þ¸Á ¸®½ºÅ©¸¦ ÁÙÀÌ°í °æÀï·ÂÀ» ³ôÀÔ´Ï´Ù.
- AI ASIC ¾ÖÇø®ÄÉÀ̼ÇÀº ÇнÀ°ú Ã߷п¡¼ À½¼º »ý¼º, ½Ç½Ã°£ ¹ø¿ª, Ãßõ ½Ã½ºÅÛ, ¿§Áö AI·Î È®ÀåµÇ°í ÀÖÀ¸¸ç, º¸´Ù ±¤¹üÀ§ÇÑ »ó¿ëÈ¿Í ¼öÁ÷Àû ½ÃÀåÀ¸·ÎÀÇ Ä§Åõ¸¦ °¡´ÉÇÏ°Ô Çϰí ÀÖ½À´Ï´Ù.
- Ĩ ¼³°è ¼ºñ½º Á¦°ø¾÷üµéÀº AI ASIC¸¦ GPU¿¡ À̾î ÁÖ¿ä AI °¡¼Ó Çϵå¿þ¾î·Î ÀÚ¸®¸Å±èÇÏ¸é¼ ¾÷°èÀÇ ¼ºÀåÀ» µÞ¹ÞħÇϰí ÀÖ½À´Ï´Ù.
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Á¦1Àå CSPÀÇ ÀÚü ¼³°è ASIC°¡ 2026³â AI ¼¹ö ½ÃÀå ¼ºÀåÀ» ÁÖµµÇÏ°í ºê·ÎµåÄİú °°Àº Ĩ Á¦Á¶¾÷üÀÇ CoWoS ¼ö¿äµµ Áõ°¡ÇÑ´Ù.
- ASIC ±â¹Ý AI ¼¹öÀÇ ºñÀ²Àº È®´ë Ãß¼¼
Á¦2Àå AI ASICÀÇ ¾ÆÅ°ÅØÃ³´Â °íÃâ·Â ¼³°è¸¦ äÅÃÇϰí Á¡Â÷ ¾×ü ³Ã°¢À¸·Î ÀüȯÇϰí ÀÖ´Ù.
- ´ëÇü CSP, ÀÚü ¼³°è AI ASIC °³¹ß °¡¼ÓÈ
Á¦3Àå ASIC ¾ÖÇø®ÄÉÀ̼ÇÀº ³»ºÎ¿¡¼ ¿ÜºÎ ºÎ¹®À¸·Î È®´ëµÇ°í, TPU´Â ¿©·¯ ±â¾÷ÀÌ ¼±ÅÃÇÑ ¼Ö·ç¼ÇÀÌ µÇ¾ú´Ù.
Á¦4Àå ±Þ¼ÓÇÑ ½ÃÀå È®´ë ¼Ó¿¡¼ AI ASICÀÌ AI Çϵå¿þ¾îÀÇ µÎ ¹øÂ° ÁÖ¿ä ¼ºÀå µ¿·ÂÀ¸·Î º¯¸ðÇÑ´Ù.
- AI ASIC ½ÃÀå ¿¬Æò±Õ ¼ºÀå·ü(CAGR)Àº ¸Å³â È®´ë Àü¸Á
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Overview
The AI ASIC market is rapidly expanding, with TPUs shifting from internal use to external commercialization due to their energy efficiency and customization. Leading cloud and tech firms actively develop in-house ASICs to reduce costs and supply risks, driving AI hardware toward high performance, low power, and diverse applications, becoming the main accelerator after GPUs.
Key Highlights:
- The AI ASIC market is experiencing rapid growth, driven by increasing cloud service demand and AI model scale.
- TPUs have advantages in energy efficiency and vertical integration, offering better cost structure and customization than high-end GPUs.
- Major US cloud providers and leading tech firms (Tesla, OpenAI, Apple) actively develop in-house ASICs tailored to specific AI applications.
- In-house ASIC development reduces long-term compute costs and supply chain risks, enhancing competitiveness.
- AI ASIC applications are expanding from training and inference to voice generation, real-time translation, recommendation systems, and edge AI, enabling wider commercialization and vertical market penetration.
- Chip design service providers support industry growth, positioning AI ASICs as the second major AI acceleration hardware after GPUs.
Table of Contents
1. CSPs' In-House Designed ASICs Will Drive Growth in AI Server Market in 2026, and CoWoS Demand from Broadcom and Other Chip Makers Will Also Rise
- Proportion of ASIC-Based AI Servers Will Climb Nearly
2. Architectures of AI ASICs Adopt High-Power Design and Gradually Shift to Liquid Cooling
- Major CSPs Are Accelerating Development of In-House Designed AI ASICs
3. ASIC Applications Branching Out from Internal to External Sectors; TPU Became the Solution Opted by Multiple Players
4. AI ASICs to Transform into Second Key Growth Engine of AI Hardware amidst Expeditious Market Expansions
- AI ASIC Market Projected for CAGR under Annual Growths