TSMC's CoWoS Capacity Emerges as Key Battleground for 2026 AI Chips, with NVIDIA Securing 60% Share
The competitive landscape for global artificial intelligence compute chips in 2026 is, more than ever, tied to the capacity allocation of a single company. According to industry analysis, the total effective capacity of Taiwan Semiconductor Manufacturing Company's (TSMC) advanced packaging technology, CoWoS (Chip on Wafer on Substrate), is expected to reach approximately 1.15 million wafers in 2026. How this limited "ammunition" is distributed among chip giants like NVIDIA, AMD, Broadcom, and MediaTek, as well as the custom ASIC (Application-Specific Integrated Circuit) demands representing tech giants like Google, Amazon, Meta, and OpenAI, will directly shape the AI compute map for the coming year.
As process technology enters the challenging deep waters of 2-nanometer nodes with soaring costs, and computing architecture diverges fundamentally between general-purpose GPUs and specialized ASICs, advanced packaging capacity has become the most critical variable in the compute power race. TSMC's CoWoS technology, which enables ultra-high-density interconnection of components like compute chiplets and High Bandwidth Memory (HBM) through heterogeneous integration, is an indispensable step in manufacturing top-tier AI chips.
Capacity Allocation: A Complex Game of Commerce and Technology
According to data from industry consulting firms cited by Tencent Technology, TSMC's monthly CoWoS capacity in 2026 is expected to gradually ramp up from 80,000 wafers at the end of 2025 to around 120,000 wafers by year-end. The average effective monthly capacity for the year is projected to be about 96,000 wafers, totaling approximately 1.15 million wafers.
In this battle for capacity, NVIDIA is expected to secure nearly 60% of the supply, or about 660,000 wafers, leveraging its status as an early co-definer and the largest investor in CoWoS technology, along with its deep process integration with TSMC, to maintain its market dominance. AMD's pre-allocated volume is about 90,000 wafers, accounting for close to 8%, representing a significant 64% increase compared to 2025.
On the other hand, the camp of design service providers creating custom ASIC chips for cloud giants is rapidly rising. Broadcom is the leader here, with its 2026 CoWoS pre-allocation surging to 200,000 wafers, a dramatic 122% year-over-year increase. This is primarily driven by Google's plans to externally supply its TPUs. Broadcom's capacity is roughly allocated among clients as follows:
| Client | Estimated Share of Broadcom's 2026 CoWoS Pre-allocation | Main Product / Notes |
|---|---|---|
| 60%-65% | TPU v6p/v7p | |
| Meta | ~20% | MTIA chips |
| OpenAI | 5%-10% | Internally codenamed Titan chip, expected launch by year-end |
Furthermore, MediaTek, as a new CoWoS customer for TSMC in 2026, will primarily handle Google's inference-focused TPU v7e chips, expected to ship in the second half of the year. Industry sources indicate that MediaTek views AI ASICs as a core future business, with its CoWoS demand potentially seeing several-fold growth in 2027.
Among other major players, Alchip, having secured orders for Amazon AWS's Trainium 3, saw its pre-allocation rise to 60,000 wafers, a 200% increase. Marvell's pre-allocation remained largely flat compared to 2025 due to some order transfers.
A Multi-Dimensional Contest: Performance, Value, and Ecosystem
However, simply comparing CoWoS wafer counts can be misleading. Due to different packaging schemes, the number of chips that can be diced from a single wafer varies dramatically. For instance, NVIDIA's previous-generation Hopper architecture used a single-die design, yielding 29 chips per wafer, while the new Blackwell generation uses a dual-die design, yielding only 14 chips per wafer. Simultaneously, the interposer area of chips is increasing to accommodate more transistors, further impacting actual compute output and cost.
From a pure compute power perspective, a single NVIDIA B300 GPU delivers up to 10 PFLOPS of FP8 performance, while the strongest custom inference ASICs (like the TPU v7p) currently offer only about half that. NVIDIA's newly announced Rubin architecture at CES 2026, claiming a 5x inference performance boost over Blackwell, suggests this performance gap may widen further.
The contrast in value dimension is even more stark. A single high-end NVIDIA GPU sells for $30,000 to $50,000, while the "internal transfer price" or external selling price of cloud giants' self-developed ASICs is much lower. For example, AI company Anthropic's purchase of 1 million TPUs from Broadcom for $21 billion translates to a per-unit cost below $15,000, less than half the price of NVIDIA's high-end products.
Therefore, comprehensively, NVIDIA is poised to use 60% of the CoWoS capacity to generate over 70% of the revenue and more than 90% of the profit in the entire AI accelerator chip market. Its advantage lies not only in the CUDA software ecosystem but also in the system-level "turnkey" solutions built with NVLink and NVSwitch.
Future Landscape: Hybrid Compute and a Protracted "Demarcation War"
Although ASICs may not lead in absolute performance or unit price, they offer significant energy efficiency and Total Cost of Ownership (TCO) advantages for specific, stable workloads like large-scale inference. This makes ASICs a critical path for hyperscale cloud providers and a few top AI companies to optimize their financials and achieve "de-NVIDIA-ization."
Industry analysis suggests the future AI compute world is more likely to adopt a "GPU + ASIC" hybrid model: tech giants using NVIDIA GPUs for cutting-edge model R&D and training, while deploying self-developed or custom ASIC chips for cost-sensitive, large-scale inference. This competition is not a fight to the death but a protracted "demarcation war" to establish respective domains of advantage.
To solidify its moat, NVIDIA has invested $20 billion to acquire AI inference chip company Groq and launched more segmented products and subscription services. Meanwhile, the ASIC ecosystem is entering a new phase of scaled deployment with Google's external TPU supply, its growth deeply tied to the capital expenditures of cloud giants.
The Ultimate Winner: TSMC, the "Arms Dealer"
Regardless of how the GPU vs. ASIC competition evolves, TSMC, as the core provider of advanced packaging capacity, remains the indispensable "arms dealer" in this chip war. Its pricing power and allocation authority over CoWoS capacity ensure the company continues to benefit from the AI wave. As the demand for compute power explodes, the scramble for advanced packaging capacity will only intensify, further cementing TSMC's strategic position.
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