Marvell and Broadcom are exposed to AI data-center infrastructure in different ways. Marvell’s disclosed profile emphasizes optical interconnects and other connectivity products alongside custom silicon, switching, and storage. Broadcom reports a much larger AI-semiconductor category that combines custom AI accelerators with networking. Neither company can be named the overall winner from the published figures because the companies report different categories for different fiscal quarters.
The practical choice depends on what you are evaluating: optical and electrical links, a customer-specific AI ASIC program, Ethernet networking, ecosystem compatibility, or the timing and visibility of a particular deployment. The comparison below separates reported results, product announcements, partnerships, and guidance so those decisions are not blurred together.
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Marvell and Broadcom are not reporting the same business
The first mistake in this comparison is putting the two headline revenue figures side by side. Marvell reported $1.651 billion in data-center end-market revenue for the fourth quarter of FY2026, up 21% year over year. Broadcom reported $16.7 billion in AI-semiconductor revenue for the third quarter of FY2026, up 221% year over year. Those figures cover different periods and different category definitions.
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| Company disclosure | Period | Reported amount | What the category includes |
|---|---|---|---|
| Marvell data-center revenue | Q4 FY2026 | $1.651 billion, up 21% year over year | Marvell’s data-center end market, including optical interconnects, custom silicon, switching and storage |
| Broadcom AI-semiconductor revenue | Q3 FY2026 | $16.7 billion, up 221% year over year | AI semiconductor demand attributed to custom AI accelerators and networking |
| Broadcom Q4 AI-semiconductor expectation | Q4 FY2026 guidance | $21.7 billion | Management guidance, not a completed quarter’s result |
Marvell’s figure is an end-market number, while Broadcom’s is an AI-semiconductor number. Broadcom’s figure may include a broader set of accelerator and networking activity than Marvell’s data-center presentation, and the fiscal quarters do not line up. These numbers show that both companies are growing in relevant areas; they do not establish relative market share, profitability, or technical superiority.
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What Marvell discloses about AI data-center infrastructure
A connectivity-heavy data-center mix
Marvell’s FY2026 fourth-quarter presentation identified sequential growth from optical interconnects, custom silicon, switching and storage. In its FY2026 proxy statement, Marvell described optical interconnect as roughly half of data-center revenue and custom silicon as approximately one-quarter. These are approximate company descriptions of the business mix, not separately audited segment results.
That mix matters for buyers evaluating the movement of data between accelerators, switches and storage. Marvell’s public positioning is not limited to an AI compute die. It spans the interfaces and communications components required to connect high-bandwidth systems, while also offering customer-specific silicon work.
The 1.6T optical roadmap
On March 12, 2026, Marvell announced an expanded 1.6T optical DSP portfolio. The announcement named the Ara, Alaska and Nova DSP platforms, along with Ethernet PHY, silicon-photonics, and linear-drive pluggable-optics (LPO) transimpedance-amplifier and laser-driver chipsets.
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The announcement is evidence of portfolio direction, not proof that every named component has reached volume production, achieved a particular deployment scale, or been adopted by a specific customer. It also does not establish that Marvell’s optical products are faster, cheaper or more reliable than a competing implementation.
Marvell’s custom-silicon role
Custom silicon is the other major element of Marvell’s disclosed data-center mix. A customer-specific ASIC can be designed around a workload, interconnect, memory architecture or system constraint rather than using an identical general-purpose device in every deployment. Marvell presents custom silicon alongside its connectivity capabilities, which can be relevant when a system designer wants the ASIC and the surrounding data-movement components to be developed with compatible requirements.
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The public material does not quantify Marvell’s custom-silicon revenue separately, disclose margins for that activity, or provide a complete list of active programs. The approximately one-quarter description in the proxy should therefore be used as a directional mix indicator rather than a precise custom-ASIC market-share figure.
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AI-semiconductor growth spans two major demand drivers
Broadcom said its Q3 FY2026 AI-semiconductor revenue reached $16.7 billion, up 221% year over year. The company attributed that demand to custom AI accelerators and networking. This is a broader AI-infrastructure framing than a disclosure limited to optical components or to one specific ASIC product line.
Broadcom’s fourth-quarter expectation of $21.7 billion is forward guidance. It should not be treated as realized revenue, a guaranteed shipment total, or a forecast for the entire fiscal year. Guidance can change with customer schedules, production capacity, qualification and demand.
Custom ASIC technology and IP
Broadcom’s FY2025 Form 10-K describes its custom-silicon solutions as providing technology and intellectual property for customer ASICs in AI and high-performance computing, networking, and storage. That description places Broadcom in the design-enablement and platform-supply role for customers developing their own silicon, rather than describing only a catalog of merchant accelerators.
The filing does not provide a clean, standalone revenue line for AI custom silicon, nor does it disclose the economics of each customer program. It establishes the scope of the offering: Broadcom’s custom-silicon capabilities are intended to support several infrastructure markets, with AI/HPC among them.
Networking in the AI-semiconductor category
Broadcom’s reported AI-semiconductor growth explicitly includes networking. That makes Broadcom relevant to both the accelerator side of an AI cluster and the network fabrics that connect compute resources. However, the disclosure does not break out how much of the $16.7 billion came from networking versus custom accelerators, and it does not provide a product-by-product shipment count.
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How the announced ecosystems differ
Marvell and NVIDIA: NVLink Fusion
Marvell and NVIDIA announced a collaboration on March 31, 2026, connecting Marvell’s custom-silicon and networking capabilities with NVIDIA’s AI ecosystem through NVLink Fusion. The announcement indicates that Marvell intends to participate in an ecosystem built around NVIDIA’s interconnect and platform approach.
An ecosystem collaboration can help a customer evaluate compatibility and integration paths, but an announcement does not prove exclusivity, volume shipments, realized revenue or superior performance. It also does not mean every Marvell custom-silicon customer will use the same interconnect or platform.
Broadcom and OpenAI
Broadcom announced an accelerator and Ethernet-networking collaboration with OpenAI on October 13, 2025. The stated scope links custom accelerator work with Ethernet networking, directly matching the two demand drivers Broadcom later cited in its AI-semiconductor reporting.
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Broadcom and Meta’s MTIA program
On April 14, 2026, Broadcom announced an extended partnership supporting Meta’s MTIA custom silicon. This is evidence of a continuing customer relationship around a hyperscaler’s internally branded accelerator effort.
As with the other announcements, the public statement does not by itself quantify shipments, financial contribution, technical results or the share of Meta’s overall infrastructure supplied by Broadcom.
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Marvell versus Broadcom by decision criterion
A useful comparison starts with the requirement being purchased. The following table uses only what the companies have publicly disclosed and marks where the evidence stops.
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| Decision criterion | Marvell | Broadcom | What a buyer should verify |
|---|---|---|---|
| Optical connectivity breadth | Disclosed optical interconnect exposure and a 1.6T DSP portfolio including Ara, Alaska and Nova, Ethernet PHY, silicon photonics, LPO TIA and laser-driver chipsets | AI networking is included in reported AI-semiconductor growth; the cited disclosures do not enumerate an equivalent 1.6T optical portfolio | Link rate, reach, power, thermal envelope, module qualification, interoperability and production availability |
| Custom silicon | Custom silicon represented approximately one-quarter of Marvell’s data-center revenue in the FY2026 proxy description | Custom-silicon solutions provide technology and IP for customer ASICs in AI/HPC, networking and storage | Process node, design-services scope, software and verification support, nonrecurring engineering terms, ownership of IP and ramp plan |
| Networking role | Data-center growth included switching and optical interconnects, in addition to custom silicon | Reported AI-semiconductor growth was driven by custom accelerators and networking; Ethernet networking was included in the OpenAI collaboration announcement | Scale-up and scale-out topology, switch or interface requirements, congestion control, latency, telemetry and software integration |
| Ecosystem evidence | Collaboration with NVIDIA through NVLink Fusion | Accelerator and Ethernet collaboration with OpenAI; extended MTIA partnership with Meta | Actual qualification status, production quantities, supported configurations and contractual scope |
| Reported financial lens | $1.651 billion Q4 FY2026 data-center revenue, up 21% year over year | $16.7 billion Q3 FY2026 AI-semiconductor revenue, up 221% year over year; $21.7 billion Q4 expectation | Comparable period, category definition, recurring versus one-time demand and contribution from the specific program |
Which company fits which infrastructure requirement?
Choose Marvell when optical and connectivity design is central
Marvell is the more natural starting point when the main problem is moving data efficiently between compute, switches and storage and the design requires a broad optical component set. Its disclosed mix gives optical interconnects unusually prominent weight, and the 1.6T announcement names products across DSP, PHY, silicon-photonics and LPO-related functions.
That does not make Marvell automatically preferable. A real evaluation still needs component samples, interoperability testing, power measurements, qualification status, software support and a supply plan. The public announcement alone cannot answer those deployment questions.
Choose Broadcom when a large custom-accelerator and networking program is the center of gravity
Broadcom’s disclosures are most relevant when the project is a hyperscaler-style custom accelerator program that also requires networking. Its reported AI-semiconductor category combines those two areas, and its filings describe customer-ASIC technology and IP spanning AI/HPC, networking and storage.
The scale implied by Broadcom’s reported category is not a guarantee that a smaller customer will receive the same service model or schedule. Buyers should ask for a program-specific statement of design resources, manufacturing allocation, qualification milestones and software responsibilities.
Use ecosystem fit as a filter, not as a verdict
The named collaborations can narrow an evaluation. A design aligned with NVIDIA’s NVLink Fusion ecosystem may make Marvell’s stated collaboration relevant. A project tied to OpenAI-style accelerator and Ethernet work or Meta’s MTIA program may find Broadcom’s announced relationships informative.
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Neither set of relationships proves that one supplier is universally compatible with every AI platform. Confirm the exact interface, firmware, compiler or driver requirements, approved component list, and support obligations for the system being built.
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Separate four kinds of evidence
- Reported results: Marvell’s $1.651 billion Q4 FY2026 data-center revenue and Broadcom’s $16.7 billion Q3 FY2026 AI-semiconductor revenue are completed-period company disclosures.
- Business descriptions: Marvell’s approximate optical and custom-silicon mix and Broadcom’s description of customer-ASIC technology explain positioning but are not independent market-share studies.
- Product roadmaps: Marvell’s 1.6T portfolio announcement identifies platforms and components but does not establish shipment volume for each item.
- Forward guidance and partnerships: Broadcom’s $21.7 billion Q4 expectation is guidance, while the NVLink Fusion, OpenAI and MTIA announcements establish stated collaboration scope rather than guaranteed revenue or exclusivity.
Ask for the program facts that public disclosures omit
- What exact device, process version or optical module is being proposed, and is it shipping in production quantities?
- Which interfaces are supported at the required rate, reach and protocol, and with which switches, accelerators and transceivers?
- What are the measured power, latency, error-rate and thermal figures under the customer’s workload?
- Which design services, verification, firmware, drivers and software tools are included?
- What are the tape-out, qualification, volume-ramp and end-of-life commitments?
- Which intellectual-property rights remain with the supplier, the customer or third parties?
- What capacity, lead time and second-source options exist if demand changes?
Common comparison errors
Do not rank the companies by the $1.651 billion and $16.7 billion figures
The figures are not like-for-like. One is a data-center end-market number for Q4 FY2026; the other is an AI-semiconductor number for Q3 FY2026. Comparing them as if they were equivalent market-share measures would overstate what the disclosures show.
Do not turn a roadmap into a shipment claim
Marvell’s Ara, Alaska and Nova DSP platforms and related optical components demonstrate a broad announced portfolio. They do not prove that every product is in volume deployment or that customers have selected every component.
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Do not treat a partnership announcement as proof of technical superiority
Marvell’s NVIDIA collaboration and Broadcom’s collaborations with OpenAI and Meta demonstrate ecosystem activity. They do not independently validate performance, pricing, exclusivity, customer satisfaction or future revenue.
Do not infer margins or market share that were not disclosed
The cited material does not quantify comparative market share, margins for the relevant product categories, independent customer satisfaction or a clean split between accelerator and networking revenue. Those questions require company-specific diligence or independent market data.
A practical evaluation framework
For a procurement or architecture review, score each candidate against the same requirements rather than using headline growth as a proxy for fit.
| Evaluation area | Suggested evidence |
|---|---|
| Optical and electrical connectivity | Validated link budget, power per port, reach, error performance, interoperability and production status |
| Custom-ASIC execution | Reference designs, design-team capacity, verification methodology, tape-out history and customer-owned requirements |
| Scale-up and scale-out networking | Supported topology, switching or interconnect protocol, congestion behavior, telemetry and software integration |
| Supply and ramp | Foundry and packaging plan, allocation, lead times, qualification gates and lifecycle commitments |
| Commercial and IP terms | Nonrecurring engineering charges, per-unit economics, licensing, indemnity, support and change-control terms |
| Ecosystem compatibility | Approved configurations and tested interoperability with the accelerator, switch, optical module and software stack actually selected |
Bottom line: the “better” choice depends on the criterion
Marvell’s public disclosures make it especially relevant to AI data-center connectivity: optical interconnects are described as roughly half of its data-center revenue, custom silicon as approximately one-quarter, and its 2026 roadmap reaches into 1.6T DSP, PHY, silicon-photonics and LPO components. Broadcom’s disclosures show a larger reported AI-semiconductor category driven by custom accelerators and networking, supported by customer-ASIC technology and IP and by announced relationships with OpenAI and Meta.
For an optical-heavy connectivity decision, Marvell’s disclosed portfolio deserves close examination. For a large custom-accelerator program that also needs networking, Broadcom’s reported scope and customer-ASIC positioning may be more directly relevant. The only defensible selection, however, comes from matching the exact architecture, qualification status, ramp schedule, software obligations and commercial terms—not from declaring a universal winner from non-comparable revenue figures.
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