Core Viewpoint - Meta is entering the ASIC market to compete with Nvidia, with plans to launch millions of high-performance AI ASIC chips by 2026, potentially challenging Nvidia's long-standing market dominance [1][2]. Group 1: Meta's MTIA Plans - Meta's MTIA project aims to release its first ASIC chip, MTIA T-V1, in Q4 2025, designed by Broadcom with a complex 36-layer PCB and hybrid cooling technology [3][8]. - By mid-2026, the MTIA T-V1.5 will double in chip area and approach Nvidia's GB200 system in computational density [3][8]. - The MTIA T-V2, expected in 2027, will feature larger CoWoS packaging and a high-power (170KW) rack design [3][8]. Group 2: ASIC Market Rise - Nvidia currently holds over 80% of the AI server market, while ASICs account for only 8-11% [7]. - By 2025, Google's TPU shipments are projected to reach 1.5-2 million units, and AWS's Trainium 2 ASICs are expected to be around 1.4-1.5 million units, potentially matching Nvidia's GPU shipments [2][15]. - With Meta and Microsoft set to deploy their ASIC solutions, total ASIC shipments may surpass Nvidia's GPU shipments by 2026 [2][15]. Group 3: Challenges and Risks - Meta's goal of 1-1.5 million ASIC shipments by late 2025 to 2026 may face delays due to wafer allocation limitations, which currently support only 300,000 to 400,000 units [4][15]. - The technical challenges of large CoWoS packaging and system debugging, which can take 6-9 months, add uncertainty to Meta's plans [4][15]. - A simultaneous acceleration in deployment by Meta, AWS, and other cloud service providers could lead to shortages of high-end materials and components, increasing costs [4][15]. Group 4: Nvidia's Advantages - Nvidia is not idle; it has introduced NVLink Fusion technology to strengthen its market position by allowing seamless connections between third-party CPUs or xPUs and its AI GPUs [5][15]. - Nvidia maintains a lead in chip computational density and interconnect technology, making it difficult for ASICs to catch up in the short term [5][15]. - The CUDA ecosystem remains the preferred choice for enterprise AI solutions, presenting a significant barrier for ASICs to overcome [5][15].