Inference and internal silicon: The two-front war against Nvidia's dominance
The AI chip market is fragmenting as major cloud providers and AI companies shift from dependence on Nvidia toward diverse silicon strategies including custom internal hardware and inference-specific designs. While Nvidia maintains a significant advantage through its CUDA software ecosystem, challengers like Amazon and AMD are capturing segments of the infrastructure market by offering specialized hardware options for cloud-based workloads.
Key Takeaways
- Nvidia's training market share remains above 90% as of late 2025, but its inference share has dipped to 60-75% due to custom silicon competition.
- Amazon has deployed 1.4 million Trainium chips across its cloud network, including over one million Trainium2 units for Anthropic's Claude models.
- Google's eighth-generation TPU 8i is its first chip specifically designed to accelerate AI agent orchestration and inference rather than general training.
- AMD's Instinct MI300 series has secured major enterprise deployments with Microsoft Azure, Meta, Dell, and HPE.
- Nvidia's CUDA ecosystem remains a critical hurdle, with more than 5.9 million developers utilizing its 20-year library of domain-specific tools.
Why It Matters
The streaming and AI infrastructure market is shifting from a hardware monopoly to a fragmented ecosystem prioritized by workload efficiency. For platform operators, this means the 'Nvidia tax'—high margins on training—is being bypassed during the more lucrative inference stage where models actually serve users. This fragmentation forces a strategic choice: stick with Nvidia’s high-cost, high-performance universal chips or port code to specialized, cheaper cloud-native silicon. Watch for the adoption rates of AMD’s MI350 and Amazon’s Trainium3 in late 2026 as indicators of whether alternative software stacks are finally bridging the CUDA gap.
Additional Context
The competitive landscape has intensified with recent hardware refreshes from both sides. Per Tom's Hardware and AMD, the Instinct MI350 series launched in Q3 2025 features 288GB of HBM3e memory, designed specifically to exceed the 180GB capacity of Nvidia’s Blackwell B200. In December 2025, AWS announced the general availability of its 3nm Trainium3 chips, which Amazon claims deliver a 4x increase in energy efficiency and 3x faster performance for real-time video generation compared to its previous generation. These moves coincide with OpenAI taking a 10% stake in AMD to secure 6GW of capacity, signaling that even Nvidia’s closest software partners are hedging their infrastructure bets.
Nvidia has responded by expanding its own ecosystem into neighboring categories. In July 2026, CEO Jensen Huang introduced the Vera CPU, a processor designed to work alongside Blackwell GPUs to capture server spending that previously went to x86 providers like Intel. Despite this, TrendForce reports that while Nvidia will drive over 70% of high-end GPU shipments in 2026 through its Blackwell series, it faces growing pressure from 'neocloud' providers and custom TPU deployments. Per Google Cloud, its eighth-generation TPUs (8t and 8i) and Axion ARM-based CPUs are now reaching volume production, specifically targeting the orchestration of 'agentic AI'—the autonomous reasoning systems expected to dominate the next phase of cloud-based applications.
Read full article at qz.com
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