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AI Compute Surges as ASICs Challenge GPU Dominance

Last Modified

2026-09-15

Update Frequency

Aperiodically

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PDF

AI compute surges in 2026 as ASICs rise, NVIDIA defends its ecosystem, and bandwidth becomes the bottleneck.

Key Highlights

Capex Surge: CSPs boost GPU buys, ASICs; rack systems land in 2027.
Training Doubles: 2026 FP16 power jumps as ASIC share erodes GPUs.
FP4 Inference Rises: New chips, MoE lift FP4 power; decode stays HBM-limited.
NVIDIA Defends Turf: MediaTek tie-up, Hugging Face deal guard its moat.

Table of Contents

Introduction
Total FP16 Computing Power of GPUs and ASICs Set for Significant Growth in 2026 amid Persisting Global Demand for AI Training Infrastructures
- US GPU Suppliers to Stay Largest Contributor in FP16 Computing Power in 2026
Global AI Chip Suppliers Turn to Increasing FP4 Compute Power Successively to Tend to Development of New AI Markets as Demand for AI Inference Booms
- FP4 Computing Power of Major AI Accelerators in 2026 to Surge to Almost 200 ZFLOPS and 511 ZFLOPS for 2027
AI Model Scale Will Determine Compute Utilization Efficiency, While Memory Bandwidth May Become the Key Bottleneck for Token Generation
- Efficient Models Deliver 6x the Token Capacity of Flagship Ones on the Same AI Accelerator Fleet
Due to Major CSPs Accelerating In-House ASIC Development, NVIDIA Is Expected to Sustain Its AI Dominance via Technology Integration and Software Ecosystem

<Total Pages: 8>

US GPU Suppliers to Stay Largest Contributor in FP16 Computing Power in 2026

Sector: AI Server / Data Center IC Design

Tag: HBM ASIC AI Chip AI Accelerator LLM AI Inference AI Server GPU AI Training Memory Bandwidth AI Capex CSP Market Share NVIDIA MediaTek AI Computing

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