Arm and NVIDIA Make Big Moves! A Paradigm Shift in CPU-GPU Interconnects

“Five years ago, there was an acquisition attempt, and five years later, a partnership!” On November 17, the chip industry was shaken by a significant collaboration — Arm announced that its CPUs can seamlessly integrate with AI chips through NVIDIA’s NVLink Fusion technology. This move is being hailed as a “century handshake”: two giants, previously thwarted by regulatory hurdles over a $40 billion acquisition, are now joining forces to rewrite the “chip connection rules” for AI data centers.

For large-scale data centers, this means no longer having to struggle with x86 CPUs; Arm architecture-based Neoverse chips can directly engage in “high-speed dialogue” with NVIDIA GPUs, while the NVLink Fusion “super data line” is quietly pulling the narrative power of AI infrastructure away from Intel and AMD.

1. Breaking the “Connection Bottleneck”: What Makes NVLink Fusion Exceptional?

First, for the non-technical audience: the CPU and GPU in an AI data center are like the “dispatch room” and the “production workshop” of a factory, and the speed of data transfer between them directly determines computational efficiency. However, the current mainstream connection methods have become a “drag on performance”.

1. The “Three Major Issues” of Traditional Connections

Most data centers currently use PCIe buses to connect CPUs and GPUs, but this system has shown its limitations in the AI era:

Insufficient Bandwidth: The bandwidth of PCIe 5.0 is only 1/14th that of NVLink, akin to using a rural road to transport rocket parts; no matter how powerful the GPU is, it has to wait for data.

High Latency: Data transfer from CPU to GPU has to pass through multiple “toll booths”, resulting in latency over three times higher than NVLink, making training large models feel like “working while taking breaks”.

Poor Compatibility: Different vendors’ CPUs and GPUs often “speak different languages” when connected, requiring additional software development for compatibility, which is both costly and time-consuming.

A Google Cloud engineer once lamented: “Connecting GPU clusters via PCIe is like tying sandbags to Bolt while running — no matter how strong the hardware, it can’t run fast.” This is why large-scale data centers have long awaited more efficient connection technologies.

2. The “Three Key Features” of NVLink Fusion

NVIDIA’s open NVLink Fusion is essentially a “super connector” tailored for AI infrastructure:

Crushing Bandwidth: The single-channel bandwidth reaches up to 1.6TB/s, which is 14 times that of PCIe 5.0, capable of simultaneously transferring the capacity of 10 4K movies.

Seamless Compatibility: By interfacing with Arm’s AMBA CHI C2C protocol, Neoverse CPUs and NVIDIA GPUs can directly “communicate” without additional adaptation.

Flexible Scalability: It supports rack-level integration, allowing hundreds of CPUs and GPUs to form a “super computing entity”, akin to consolidating small workshops into a large factory.

More importantly, it is “open” — previously, NVLink only supported NVIDIA’s own CPUs, but now Arm chips can also connect, providing data centers with a more “cost-effective option”.

2. The “Calculations” of the Giants: A Perfect Exchange of Interests

The collaboration between Arm and NVIDIA appears to be a “match made in heaven”, but in reality, it is a precise “exchange of interests”, with both parties leveraging each other’s strengths to compensate for their weaknesses.

1. Arm’s “Breakthrough Strategy”: Seizing Data Center Market Share via NVLink

Arm is a “dominant player” in the mobile sector, but has always been a “minor player” in data centers — currently, x86 architecture holds 90% of the server market, while Arm only accounts for 10%. By partnering with NVIDIA, Arm aims to turn the tables:

Compensating for Performance Shortcomings: Although Neoverse CPUs have high energy efficiency, their previous connection efficiency with GPUs was low; with NVLink integration, they can achieve “Grace Blackwell-level performance”, directly competing with NVIDIA’s own Grace CPU.

Capturing Customer Resources: NVIDIA GPUs hold over 80% market share in AI data centers, and with NVLink, Arm can seamlessly enter the supply chains of giants like Microsoft and Google — both of which are building customized data centers based on Neoverse.

Aiming for Market Goals: Arm has set a target to capture 50% of the large-scale data center market by 2025; without NVIDIA’s connection technology support, this would be nearly impossible.

Moreover, there is a tangible revenue growth opportunity: Arm’s “Cloud and Networking” business generated only $303 million in revenue for the fiscal year 2023, but with increased shipments of Neoverse chips, this revenue is expected to double by fiscal year 2025.

2. NVIDIA’s “Ecosystem”: Open for Monopoly, the More Open, the More Monopolistic

NVIDIA appears to be “giving benefits”, but in reality, it is setting up a larger game:

Consolidating GPU Dominance: Opening NVLink allows more customers to choose NVIDIA GPUs — after all, switching GPUs means re-adapting connection technologies, which is too costly.

Seizing Connection Standard Authority: If NVLink Fusion becomes the industry standard, in the future, whether it’s Arm or other vendors’ CPUs, they will have to look to NVIDIA for approval, which is a more sustainable profit model than selling chips.

Addressing Regulatory Pressure: NVIDIA learned from the failed acquisition of Arm in 2020 that “going solo” is not feasible; adopting a collaborative model avoids regulatory risks while still maintaining control over the ecosystem.

Jensen Huang’s statement carries deep meaning: “NVLink Fusion is the connection structure of the AI era” — translated, it means “how AI data centers connect in the future, I will have the final say”.

3. Industry Shockwaves: The x86 Camp is Alarmed, Cloud Giants are Smiling

This collaboration is like throwing a bomb into a calm lake, sending ripples that affect everyone from chip manufacturers to end customers.

1. Intel and AMD: Are Good Days Coming to an End?

The two giants of the x86 camp are the first to bear the brunt, facing a double whammy:

Market Share Erosion: Arm CPUs have 30% higher energy efficiency than x86, and now that the connection issue with GPUs has been resolved, Arm is projected to account for 35% of the new computing power added by Google Cloud in 2024; at this rate, x86’s 90% market share will inevitably be chipped away.

Loss of Technical Advantage: Intel originally hoped to retain customers with its Ultra Path Interconnect technology, but with NVLink Fusion now open, customers can easily switch to the Arm + NVIDIA combination at a lower cost.

Forced to Follow Trends: AMD recently launched its 192-core Bergamo series CPUs, aiming to capture the high-density computing market, but now that Arm and NVIDIA have teamed up, they can only hastily accelerate cooperation with third-party connection technologies.

Analysts have joked: “Previously, Arm couldn’t compete with x86; now, Arm, with NVIDIA, is taking on x86 — they are simply not on the same level.”

2. Large-Scale Data Centers: A “Double Surprise” of Cost Savings and Speed Increase

For cloud giants like Microsoft and Google, this collaboration is like “a windfall”:

Significant Cost Reduction: Arm CPUs are 20% cheaper than x86, and with NVLink Fusion reducing transmission energy consumption, data center operating costs can drop by over 15%. Based on Google Cloud’s annual $5 billion computing expenditure, this could save $750 million a year.

Computational Power Surge: With improved connection efficiency, GPU utilization rates can increase from 70% to over 95%, effectively providing an additional 25% of free computational power for the same expenditure.

Customization Freedom: Previously, using Arm CPUs meant enduring connection bottlenecks; now, they can freely combine Arm + NVIDIA and design custom solutions based on Neoverse without being tied to x86.

AWS has long been quietly preparing: its self-developed Graviton chips are based on Arm architecture, and now with NVLink Fusion, the speed of training large models has doubled. It’s no wonder that the CEO of Amazon Cloud privately stated, “This is the most cost-effective technology collaboration of the year.”

3. Industry Chain Following Suit: The Arm Ecosystem is Entering an “Explosive Period”

This collaboration acts as a “traffic light”, energizing the entire Arm ecosystem:

Chip Design Manufacturers: Qualcomm and Fujitsu immediately announced plans to design customized CPUs based on Neoverse, targeting the data center market — Fujitsu’s 2nm MONAKA CPU even boasts a slogan of “doubling energy efficiency”.

Foundry Manufacturers: TSMC and GlobalFoundries are expanding their Arm chip production lines, with TSMC planning to increase Arm foundry capacity by 40% by 2025, fearing they won’t keep up with orders.

Software Manufacturers: Many AI frameworks previously lacked support for Arm; now, Microsoft and Google are leading the adaptation, with mainstream frameworks like PyTorch and TensorFlow rushing to develop optimized versions for Arm.

4. Many Concerns: Can This “Marriage” Last?

While it seems to be a bright future, the collaboration between Arm and NVIDIA hides several “landmines”, and whether they can last is still uncertain.

1. The “Pitfalls” of Technical Adaptation: Can “Seamless Connection” Really Be Achieved?

Theoretically, NVLink Fusion can perfectly interface with Arm CPUs, but practical applications may encounter issues:

Different manufacturers’ CPUs designed based on Neoverse can vary significantly, making unified adaptation challenging, potentially leading to situations where “some can connect, some cannot”.

Rack-level integration requires addressing a series of issues such as heat dissipation and power supply, which poses significant challenges for data center renovations, making it difficult for small companies to participate.

A senior engineer revealed: “Currently, it can only be achieved in the lab; large-scale commercial use will take at least 1-2 years, during which many pitfalls will need to be navigated.”

2. The “Contradictions” of Interest Distribution: Who Will Be the “Decision Maker”?

Arm wants to leverage NVIDIA’s ecosystem to gain market share, while NVIDIA seeks to solidify standards with Arm’s chips; if their goals conflict, it could lead to a fallout:

If Arm’s market share exceeds expectations, will it demand NVIDIA to open up more technologies?

If NVIDIA uses its control over standards to suppress Arm’s partners, will Arm ally with other manufacturers to start anew?

The failed acquisition attempt in 2020 has already proven that the interests of these two giants are not entirely aligned; the current collaboration resembles a “temporary alliance” that could dissolve if interests diverge.

3. The “Regulatory Noose”: Will They Be Thwarted Again?

Although this is a collaboration rather than an acquisition, regulatory bodies may not sit idly by:

NVIDIA’s GPU market share exceeds 80%, and Arm holds a monopoly in mobile chips; their partnership could create a “dual monopoly” in “connection technology + chip architecture”.

The U.S. FTC has always been sensitive to “vertical integration”; having previously blocked Microsoft’s acquisition of Activision Blizzard, it may now focus on the standard-setting authority of NVLink Fusion.

If regulators intervene, the collaboration between these two giants could hit the brakes at any moment.

5. Conclusion: The “Connection Revolution” in AI Infrastructure is Just Beginning

The collaboration between Arm and NVIDIA is essentially a “battle for ecological positioning” — in the face of an explosion in AI computing demand, whoever controls the standards for chip connections will hold the lifeblood of AI infrastructure. This move not only gives Arm hope for breaking the x86 monopoly but also brings NVIDIA closer to its goal of becoming the “AI ecosystem ruler”.

In the coming years, we will see more data centers adopting the Arm + NVIDIA combination, forcing the x86 camp to accelerate technological innovation, while NVLink Fusion is likely to become the industry standard. However, this revolution will not be smooth sailing; technical adaptation pitfalls, contradictions in interest distribution, and regulatory pressures will all serve as “stumbling blocks” along the way.

What is certain is that the “connection era” of AI chips has arrived. Previously, the competition was about the single-machine performance of CPUs and GPUs; now, it is about the connection efficiency of entire clusters. The partnership between Arm and NVIDIA undoubtedly sets new rules for this competition — as for who will ultimately prevail, it depends on who can truly transform the “connection advantage” into “ecological hegemony”. After all, in the AI era, having fast chips is not enough to win; the real skill lies in being able to “coordinate seamlessly” with teammates.