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NVIDIA vs. Custom ASICs: GPU Dominance Persists Despite Startup Performance Claims · history

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2026-06-21 18:21 UTC · 48 items

What

NVIDIA has held or grown AI compute market share against custom ASICs as of mid-2026 [1], while its NVLink Fusion ecosystem has expanded to include Samsung Foundry as a manufacturing partner [6] and SiFive for RISC-V-based AI data center chips [7], alongside earlier participants like Marvell. NVIDIA's strategy positions custom silicon makers as participants in its interconnect infrastructure rather than competitors to its GPU business. Startup performance claims — Tensordyne's 13x throughput [9] and DeepAdapt's 82% cost reduction [10] — remain unverified by any third party.

Why it matters

Samsung Foundry's addition means NVIDIA's ecosystem reach now extends into the manufacturing layer, not just chip design. If custom silicon — from hyperscaler ASICs to RISC-V chips — is increasingly built for and connected via NVLink Fusion, NVIDIA captures value from the growth of custom compute rather than competing against it.

Open questions

  • Does NVLink Fusion's expansion to Samsung Foundry and SiFive indicate NVIDIA is building a standard that captures the custom silicon manufacturing chain, or are these partners adopting it only for narrow use cases? [6][7]

  • Will Tensordyne's 13x throughput claim or DeepAdapt's 82% cost reduction survive independent testing, given that both rest on unverified internal benchmarks? [9][10]

  • Meta and Amazon are expanding custom silicon with captive workloads and engineering scale — will their chips remain within NVIDIA's NVLink Fusion ecosystem or provide a path to eventual independence? [11][12]

  • What specific mechanism explains NVIDIA's market share resilience — CUDA depth, supply chain positioning, NVLink Fusion lock-in, or some combination? [1][3]

Narrative

For roughly two years, the dominant expectation in AI compute markets was that custom ASICs — built by hyperscalers and startups alike — would gradually absorb workloads from NVIDIA GPUs. Mid-2026 data has not confirmed that prediction: NVIDIA has held or grown its market share against ASICs [1], and Jensen Huang has publicly projected continued rapid growth into the following year [2]. The analytical explanation that has gained the most traction centers on software rather than silicon. SemiAnalysis states directly that AI chip competition is decided by software ecosystem depth: 100% of chip startups publish slides with impressive simulated benchmark numbers, but 99% fail in production because building a competitive software stack alongside the chip is the actual hard problem — one that NVIDIA's CUDA platform, accumulated over roughly two decades, has largely solved [3].

NVIDIA has also moved to make custom silicon a structural participant in its own ecosystem rather than an external competitor. NVLink Fusion allows hyperscalers and chip designers to integrate non-NVIDIA compute silicon into NVIDIA-led clusters [4][5]. Jensen Huang appeared at a Marvell keynote to explain this coexistence model, and the ecosystem has since expanded: Samsung Foundry joined as a manufacturing partner [6], SiFive integrated NVLink Fusion for RISC-V-based AI data center chips [7], and Astera Labs — which makes connectivity silicon for AI clusters — published analysis framing NVLink Fusion as establishing connectivity as the central layer of AI infrastructure [8]. The practical effect is that custom silicon makers, whether designing chips or manufacturing them, increasingly operate within NVIDIA's interconnect framework rather than outside it.

The startup benchmark cycle continues alongside these structural developments. Tensordyne published a claim of 13x rack throughput versus NVIDIA's NVL72 GB300 on DeepSeek-R1 workloads, based on internal simulations with no independent validation [9]. DeepAdapt separately claims its runtime layer cuts AI operating costs by 82% and delivers 33x faster inference by shifting workloads from GPUs to standard CPUs [10] — also without third-party verification. Both fit the pattern SemiAnalysis describes: large numbers on slides, simulation basis, no external confirmation [3].

Hyperscalers remain a structurally distinct case. Meta has expanded its MTIA custom silicon program to power next-generation AI workloads [11], and Amazon is entering the AI chip market [12]. Unlike independent startups, these organizations have the engineering scale and captive workloads to develop supporting software stacks alongside custom hardware. Whether their chips operate within the NVLink Fusion ecosystem or eventually provide a path to independence from NVIDIA is unresolved. China's domestic AI supply chain — covering compound semiconductors and Huawei's architecture work [13] — operates outside this ecosystem frame entirely, as a separate competitive track.

Timeline

  • 2026-01-15: SiFive announces integration of NVLink Fusion for next-generation RISC-V AI data center chips. [7]
  • 2026-03: Meta announces expansion of its MTIA custom silicon program to power next-generation AI workloads. [11]
  • 2026-06-14: NVIDIA CEO Jensen Huang states publicly that next year's growth will remain rapid. [2]
  • 2026-06-15: Analysis published on China's growing supply chain independence in AI hardware, covering compound semiconductors and Huawei's architecture work. [13]
  • 2026-06-17: Tensordyne announces an inference rack claiming 13x rack throughput versus NVIDIA's NVL72 GB300 on DeepSeek-R1, based on internal simulations. [9][16]
  • 2026-06-18: Milk Road AI publishes analysis arguing data shows NVIDIA has held or grown market share against ASICs, contrary to two years of consensus predictions. [1]
  • 2026-06-19: Jensen Huang appears at a Marvell keynote to explain connectivity's role in AI systems, framing NVLink Fusion as enabling custom silicon coexistence within NVIDIA-led clusters. [4]
  • 2026-06-19: Amazon reported to be entering the AI chip market. [12]
  • 2026-06-19: SemiAnalysis states that 99% of custom ASIC projects fail and that AI chip success is determined by software depth, not hardware specifications. [3]
  • 2026-06-19: DeepAdapt claims its runtime layer cuts AI operating costs by 82% and delivers 33x faster inference by shifting workloads from GPUs to CPUs, without independent validation. [10]
  • 2026-06-20: Milk Road AI highlights Marvell's transformation from consumer chip maker to major AI semiconductor supplier via $36 billion in acquisitions over a decade. [15]
  • 2026-06-20: SemiAnalysis argues AI networking's copper-vs-optics binary framing oversimplifies the actual landscape and advocates for a GPU cluster architecture lens. [14]
  • 2026-06-21: Samsung Foundry added to NVIDIA's NVLink Fusion ecosystem as a manufacturing partner for custom silicon. [6]
  • 2026-06-21: Astera Labs publishes analysis framing NVLink Fusion as making connectivity the central layer of AI infrastructure. [8]

Perspectives

NVIDIA / Jensen Huang

Projects continued rapid growth; NVLink Fusion positions custom silicon makers as ecosystem participants rather than competitors, reframing NVIDIA's role as the interconnect backbone of AI compute.

Evolution: Consistently bullish on growth; the NVLink Fusion strategy has expanded from enabling a few large players to encompassing a broad set of chip designers and foundries.

[2][4][5]

SemiAnalysis

AI chip competition is decided by software ecosystem depth, not hardware benchmarks; 99% of ASIC projects fail in production regardless of simulation results. Also argues AI networking copper-vs-optics binary is an oversimplification.

Evolution: Consistent skeptic of hardware-first ASIC narratives; extended the analysis to networking investment framing.

[3][14]

Milk Road AI

Data shows NVIDIA has held or grown market share against ASICs; the 'NVIDIA versus everyone' competitive frame is wrong and NVLink Fusion makes custom silicon makers ecosystem participants.

Evolution: Contrarian on ASIC displacement; explicitly reframes competition as coexistence via NVLink Fusion.

[1][4][15]

NVLink Fusion ecosystem participants (Marvell, SiFive, Samsung Foundry)

Marvell builds hyperscaler ASICs, SiFive integrates NVLink Fusion for RISC-V AI data center chips, and Samsung Foundry manufactures within the ecosystem — all treating NVLink Fusion as shared infrastructure rather than competing against NVIDIA.

Evolution: Expanded from Marvell alone to a growing set of chip designers and a major foundry, showing the ecosystem now spans manufacturing as well as design.

[15][4][6][7]

Astera Labs

Frames NVLink Fusion as establishing connectivity as the central layer of AI infrastructure — a position that aligns with their own connectivity silicon business.

Evolution: New voice in this thread; their stake in connectivity makes the framing self-interested, though it is directionally consistent with the evidence from other sources.

[8]

Meta

Treating custom silicon (MTIA) as critical to scaling next-generation AI; expanding internal chip program as a supplement or partial replacement for external GPU procurement.

Evolution: Deepening commitment to custom silicon through 2026.

[11]

Tensordyne / DeepAdapt

Both claim large performance advantages over NVIDIA-centric inference (13x throughput and 82% cost reduction respectively), based on internal simulations without independent validation.

Evolution: Fit the benchmark-slide pattern SemiAnalysis describes; neither claim has been independently verified.

[9][10]

Sky Rain

China's positions in compound semiconductors and Huawei's architecture work are building a supply chain track that operates outside the NVIDIA-dominated ecosystem.

Evolution: Consistent focus on China supply chain independence as a structurally distinct competitive thread.

[13]

Tensions

  • Milk Road AI and NVIDIA argue the 'NVIDIA versus everyone' frame is wrong — NVLink Fusion makes custom silicon a coexistent participant; SemiAnalysis supports NVIDIA's resilience but through a different mechanism (software moat, not ecosystem embrace). [4][1][3]
  • SemiAnalysis argues ASIC startup performance claims are essentially marketing noise — 99% of projects fail regardless of benchmark slides — while Tensordyne and DeepAdapt both publish large unverified numbers framed as breakthroughs. [3][9][10]
  • Milk Road AI argues mid-2026 data shows NVIDIA holding or growing market share, directly contradicting the two-year consensus prediction that custom silicon would erode NVIDIA's position. [1]
  • The software moat argument holds that startups cannot overcome CUDA's depth; Meta and Amazon's hyperscaler buildout — with captive workloads and engineering scale — tests whether organizations with sufficient resources can develop the required software stack alongside custom hardware. [3][11][12]
  • Tensordyne's 13x throughput claim and DeepAdapt's 82% cost reduction both remain unverified by any third party; the gap between simulation-based numbers and any independent result is unresolved. [9][10]

Sources

  1. [1] Everyone assumed Nvidia would get crushed by ASICs but the data says the opposite just happened and the reason why chang… — Milk Road AI Twitter (2026-06-18)
  2. [2] 🚨 $NVDA -NVIDIA CEO JENSEN HUANG: NEXT YEAR’S GROWTH WILL REMAIN RAPID 🚀🧠 — reactive:asic-gpu-market-dynamics (2026-06-14)
  3. [3] 100% of AI chip startups have slides/“simulated performance data” showing that their chip is way better, but 99% of cust… — SemiAnalysis Twitter (2026-06-19)
  4. [4] Jensen Huang showed up at a Marvell keynote to explain why connectivity matters (Save this). — Milk Road AI Twitter (2026-06-19)
  5. [5] Build Semi-Custom AI Infrastructure | NVIDIA NVLink Fusion — reactive:asic-gpu-market-dynamics
  6. [6] NVIDIA Adds Samsung Foundry to NVLink Fusion Ecosystem for Custom Silicon Manufacturing : r/hardware — reactive:asic-gpu-market-dynamics
  7. [7] SiFive To Integrate Nvidia NVLink Fusion For Next-Gen AI Data Centers — reactive:asic-gpu-market-dynamics
  8. [8] Why Connectivity is the New Frontier of AI Infrastructure—and What NVLink Fusion Means for the Future — reactive:asic-gpu-market-dynamics
  9. [9] Quite a massive inferencing rack breakthrough from @TensordyneInc . — Rohan Paul Twitter (2026-06-17)
  10. [10] DeepAdapt has launched a runtime intelligence layer that cuts AI operating costs by up to 82% and 33X faster inference b… — Rohan Paul Twitter (2026-06-19)
  11. [11] Expanding Meta's Custom Silicon to Power Our AI Workloads — reactive:asic-gpu-market-dynamics
  12. [12] 🚨 AMAZON IS ENTERING THE AI CHIP MARKET — reactive:asic-gpu-market-dynamics (2026-06-19)
  13. [13] China’s Quiet Dominance in the AI Supply Chain: InP, Semiconductor Independence, and Huawei’s Logic Folding Wildcard — reactive:asic-gpu-market-dynamics (2026-06-15)
  14. [14] Investors have increasingly framed AI networking as a binary debate between copper and optics. Thus they are constantly … — SemiAnalysis Twitter (2026-06-20)
  15. [15] In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). — Milk Road AI Twitter (2026-06-20)
  16. [16] Tensordyne Announces Breakthrough Inference System - LinkedIn — reactive:asic-gpu-market-dynamics