Intel sells a broad data-center platform that includes CPUs, Gaudi AI accelerators and networking products; Marvell focuses on custom silicon designed with hyperscalers and the high-speed electrical and optical links that connect AI systems. They are both exposed to AI infrastructure spending, but they are not like-for-like chip vendors: Intel reports a segment spanning several product categories, while Marvell reports data-center revenue and product mix. That distinction matters when comparing their products or financial figures.
Intel vs. Marvell AI chips: what each company makes
| Comparison | Intel | Marvell |
|---|---|---|
| Core AI role | Branded Gaudi accelerators, alongside Xeon CPUs and other data-center products | Custom compute silicon designed with customers, plus connectivity components and IP |
| Who shapes the design | Intel defines product families and works with OEMs on systems | Hyperscaler customers help specify custom XPUs, CPUs and infrastructure devices |
| Where it fits in a system | Host and general-purpose compute, accelerators, networking and custom ASICs | Custom compute, packaging and the electrical and optical connections around it |
| How financial reporting groups the business | Data Center and AI (DCAI) segment, which covers more than AI accelerators | Data-center end-market revenue and product mix |
Intel describes DCAI as covering x86 CPUs, AI accelerators, NICs, IPUs and custom ASICs for cloud, enterprise, telecommunications and high-performance computing. Its reported DCAI revenue therefore is not a standalone measure of AI-accelerator sales. Intel’s 2025 financial results describe the segment and its scope.
What Intel offers for AI data centers
Gaudi accelerators
Intel positions Gaudi 3 for large-scale generative AI training and inference. In its April 9, 2024 announcement, Intel listed a 5 nm design, 128 GB of HBM2e, 3.7 TB/s of memory bandwidth and 24 integrated 200 Gb Ethernet ports. These are Intel-published specifications, not an independent evaluation. Intel also says Gaudi supports PyTorch and Hugging Face models, and describes a PCIe card for fine-tuning, inference and retrieval-augmented generation. Intel’s Gaudi 3 announcement gives its product and software positioning.
CPUs, networking and systems
Intel’s AI role is not limited to accelerator cards. Xeon CPUs can act as host and general-purpose compute in AI infrastructure, while NICs, IPUs and other products contribute networking and infrastructure functions. Intel’s Q2 2026 update described rack-scale and disaggregated inference solutions built on Xeon processors alongside the Xeon 6+ data-center CPU launch. Intel’s Q2 2026 earnings release is the source for that update.
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OEM deployment
Intel named Dell, HPE, Lenovo and Supermicro as OEMs expected to bring Gaudi 3 to market. In May 2025, Intel described a Dell AI platform using Gaudi 3, including an eight-accelerator server configuration. That offers a route to deployment through enterprise systems rather than implying that every buyer will purchase a standalone card. Availability can vary by system, region and date; check the OEM for current configurations. Intel’s May 2025 availability announcement describes the OEM plans.
What Marvell makes for AI data centers
Custom compute designed with customers
Marvell’s clearest AI-compute role is building custom ASICs and XPU designs to customer specifications, rather than selling a broadly branded accelerator comparable to Gaudi. Its fiscal 2025 annual report describes platform IP including high-speed SerDes, Arm compute, security, silicon photonics, chiplet and die-to-die technology, co-packaged optics and custom HBM approaches. That filing reported multiple completed 5 nm designs, work progressing through 3 nm designs, and development of a 2 nm platform; those are status statements from that filing, not a guarantee of Marvell’s current process roadmap. Marvell’s fiscal 2025 Form 10-K describes its custom-silicon business and platform IP.
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Marvell’s June 2025 announcement described a custom accelerator package combining XPU compute silicon, HBM, other chiplets and silicon-photonics engines. Its portfolio also includes PCIe retimers, CXL devices, cable and optical DSPs, coherent DSPs and data-center interconnect modules. In practical terms, Marvell’s role can span both the customer-specific compute design and the links needed to move data into, out of and between systems. The company’s stated bandwidth and power comparisons for its 6.4T silicon-photonics engine are component-level claims, not a direct measure of full-system AI performance. Marvell’s co-packaged optics announcement describes that architecture and its claims.
Hyperscaler relationships
In a corrected May 29, 2025 release, Marvell said it was collaborating with all four top hyperscalers on custom XPUs and CPUs, network-interface controllers, CXL controllers and other infrastructure devices. The statement did not name those customers, so it does not establish which companies are involved or the scale or timing of any individual program. Marvell’s corrected release is the source.
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| Reported figure | What it measures | What it does not establish |
|---|---|---|
| $16.9 billion in FY2025, up 5% from FY2024 | Intel DCAI segment revenue for fiscal 2025; it includes servers and networking as well as AI accelerators and other products. Intel FY2025 results | Intel AI-accelerator revenue by itself |
| $6.3 billion in Q2 2026, up 59% year over year | Intel DCAI segment revenue in that quarter; segment revenue includes intersegment transactions. Intel Q2 2026 earnings release | Gaudi sales alone or a full-year result |
| More than $6 billion in FY2026 | Marvell data-center revenue, as reported in its May 2026 proxy statement. Marvell FY2026 proxy | A directly equivalent Intel segment measure |
| About three-quarters of FY2026 total revenue | Marvell’s data-center share of total revenue, per its May 2026 proxy | The share attributable specifically to AI compute |
| About 25% of FY2026 data-center revenue | Marvell’s custom-silicon share of its data-center revenue, per its May 2026 proxy | A standalone amount directly comparable with Intel DCAI |
| Roughly half of FY2026 data-center revenue | Marvell’s optical-interconnect share of its data-center revenue, per its May 2026 proxy | AI accelerator revenue or a measure of compute performance |
The periods and business definitions differ: Intel’s FY2025 DCAI figure is a broader operating segment, while Marvell’s FY2026 data-center figures describe an end market and its product mix. Marvell’s custom-silicon and optical-interconnect percentages are shares within its own data-center business, not rival-company totals that can simply be added to or matched against Intel’s DCAI revenue. The available disclosures do not provide a clean standalone Intel AI-accelerator revenue figure or a directly comparable Marvell AI-chip revenue figure.
Intel’s FY2025 filing also reported $922 million in Gaudi AI-accelerator inventory-related charges recognized in 2024, and said 2025 DCAI operating income benefited from lower Gaudi inventory-related charges versus 2024. Those accounting disclosures are relevant context, but they do not by themselves establish current Gaudi demand. Intel’s fiscal 2025 Form 10-K contains the filing details.
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What this means when evaluating the two businesses
Intel offers an identifiable product family and OEM system path across CPUs, accelerators and infrastructure. Marvell’s AI exposure is more tied to customer-specific silicon programs and the connectivity and packaging components around those systems. That makes product announcements and revenue disclosures answer different questions: a custom-silicon design win is not automatically a broadly available product, and a data-center revenue share is not a direct measure of accelerator sales.
- For product comparisons: Match workload, model, precision, system size, networking, power, software stack, availability and system price. Intel’s Gaudi comparisons against Nvidia products in its launch material are projections for specified models and workloads, not an independent universal ranking. Its May 2025 Dell price-performance claim was for a particular Llama 3 80B configuration and came with test-data and pricing caveats.
- For ecosystem and deployment: Consider whether a buyer wants a defined Intel/OEM platform and its software path, or whether a hyperscaler is developing a custom design with Marvell. These are different procurement and engineering models.
- For business analysis: Keep fiscal periods, segment definitions and product categories attached to every revenue figure. Neither company’s cited figures provide an apples-to-apples AI-chip revenue comparison.
There is no independently verified cross-vendor benchmark establishing an overall Intel-versus-Marvell performance winner. The meaningful comparison depends on the specific system and workload, while the business comparison is chiefly between Intel’s broader, branded data-center platform and Marvell’s customer-specific compute plus connectivity exposure.
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