Will NVIDIA NVLink Fusion Truly Reshape the Industry Paradigm?
This was originally posted on X on May 21, 2025.
At the recent Computex Taipei, NVIDIA CEO Jensen Huang emphasized the concept of the AI Factory, presenting a vision that goes beyond chip design or data center construction and instead outlines the evolution of an integrated architecture across the entire stack — from CHIP → SYSTEM → DC → INFRA
At the core of this vision lies NVLink Fusion, which is positioned as a fundamental technology. While industry experts agree that this could redefine next-generation AI infrastructure, there are still significant challenges in terms of practical implementation and ecosystem integration.
Technical Barriers to Heterogeneous Architecture Integration CUDA Ecosystem Compatibility Challenges
Integrating third-party ASICs into NVIDIA’s CUDA ecosystem is far more complex than it may appear. Cutting-edge GPU architectures like Blackwell are deeply optimized for CUDA, with tightly coupled memory access patterns, scheduling mechanisms, and API interfaces.
Bringing heterogeneous ASICs into this environment raises issues beyond simple physical connectivity. Achieving a consistent memory model, ensuring software stack compatibility, optimizing data transfer paths, and minimizing latency are all monumental tasks. In particular, integrating NVLink with external CPUs — as opposed to NVIDIA's in-house Grace CPUs — could require a fundamental redesign of the overall system architecture.
Questioning Workload Suitability
It’s also questionable whether the integration of GPUs and ASICs is truly optimized for real-world industrial workloads. GPUs are best suited for general-purpose parallel processing, while ASICs excel in highly specialized tasks. Although a hybrid system combining the two aspires to be a "universal platform," the economic viability of such heterogeneous architectures remains unclear, especially if the demand for these workloads is not large enough to justify the complexity and cost.
Such architectures not only increase development complexity and system costs but may also yield limited real-world performance benefits. Effectively utilizing heterogeneous computing environments would likely require the development of domain-specific languages (DSLs), which could in turn demand a fundamental rethinking of CUDA itself.
Strategic Use Cases for NVLink Potential of NVL72 as a Switch Fabric
A more pragmatic approach might be to focus on building a switch fabric centered around NVL72. This structure could connect various ASICs and CPUs without the need for NVIDIA GPUs, resembling the modular routing/switching platforms seen in Cisco's GSR or Catalyst 6500 series in the past.
This modular approach — with a NVLink-based backplane linking diverse compute resources — is certainly intriguing. However, when compared to integrated platforms like the DGX-B200 with eight GPUs, it may fall short in terms of cost efficiency. NVIDIA's move to expand NVLink Fusion to external partners could be a strategic attempt to distribute the high costs associated with GB200-based systems.
Memory Expansion and Bridge Chip Strategy
The most immediate implementation scenario may involve NVLink-based memory expansion. If memory manufacturers such as Samsung or Micron develop NVLink-compatible external memory modules that connect directly to GPUs, this could help alleviate memory bottlenecks in large-scale AI models. It would supplement expensive HBM memory while enabling greater capacity scalability.
Another promising direction is the development of NVLink-to-PCIe/CXL bridge chips. If NVIDIA or companies like AsteraLabs produce such chips to transition existing PCIe ecosystems to NVLink fabrics, the impact would go beyond performance improvements — it would significantly expand NVIDIA's influence over system interconnect architectures. This could pose a serious threat to traditional interconnect providers like Broadcom.
Conclusion: Technical Ambition vs. Ecosystem Realignment
NVIDIA’s NVLink Fusion strategy is a bold attempt to redefine the paradigm of AI infrastructure. However, this is not merely a case of introducing new technology — it involves a wholesale reconfiguration of the platform ecosystem. It remains uncertain how smoothly technical integration with partners like AsteraLabs, Marvell, and MediaTek will proceed, and how enterprise customers will respond to these architectural shifts.
What is clear, however, is that the race for AI infrastructure dominance is already well underway. We can expect to see rapid acceleration in both technological innovation and shifts in industry dynamics. Whether NVLink Fusion becomes the industry standard as NVIDIA envisions — or whether it remains confined to a more limited role — will likely be determined by adoption rates over the next 1–2 years. How legacy interconnect giants like Broadcom navigate this wave of disruption will also be a key storyline to watch.