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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Intel sells a broad data-center platform that includes CPUs, Gaudi AI accelerators, networking products and custom silicon. Marvell’s AI business is more centered on custom compute designed with hyperscalers, plus the electrical and optical connectivity that links large AI systems. They are both exposed to data-center AI spending, but they do not sell the same kind of product, and their reported financial figures are not directly comparable.
Intel vs. Marvell AI chips: the main difference
Intel offers named, Intel-branded product families, including its Gaudi accelerators and Xeon CPUs, alongside networking and infrastructure products. Marvell’s clearest AI-compute role is to co-design custom silicon to a customer’s specifications; it also sells interconnect products and licenses or supplies platform IP. That makes Intel more visible as a provider of complete, defined product lines, while Marvell is more often an ingredient in a hyperscaler’s own system design.
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| Comparison | Intel | Marvell |
|---|---|---|
| Compute products | Xeon CPUs and Gaudi AI accelerators, plus custom ASICs. | Customer-specific ASICs and XPU designs, including AI compute built to customer requirements. |
| Connectivity and infrastructure | NICs, IPUs and other data-center infrastructure products. | Electrical and optical interconnect components and IP, including SerDes, retimers, cable DSPs and silicon-photonics technologies. |
| Typical design relationship | Defined Intel product families, including systems offered through OEMs. | Custom silicon and platform technologies developed with customers, particularly hyperscalers. |
| What the companies’ reported figures cover | Intel’s Data Center and AI (DCAI) segment includes multiple product categories, not just AI accelerators. | Marvell reports data-center as an end market, with custom silicon and optical interconnect among its data-center products. |
What does Intel make for AI data centers?
Intel describes DCAI as a portfolio based on x86 architecture, spanning CPUs, AI accelerators, network interface cards (NICs), infrastructure processing units (IPUs) and custom ASICs. Its customers include cloud and enterprise data centers, telecommunications providers and high-performance computing users. Because the segment bundles those businesses, DCAI revenue cannot be read as Gaudi revenue or as a standalone measure of Intel’s AI-accelerator sales. Intel’s FY2025 results describe the segment and its scope.
Gaudi 3 is Intel’s named AI accelerator
Intel positions Gaudi 3 for large-scale generative-AI training and inference. In its April 2024 announcement, Intel specified a 5-nanometer design, 128 GB of HBM2e memory, 3.7 TB/s of memory bandwidth and 24 integrated 200 Gb Ethernet ports. Intel also described support for PyTorch and Hugging Face models, and a PCIe card aimed at fine-tuning, inference and retrieval-augmented generation (RAG). These are Intel-published specifications and positioning, not an independent assessment of performance. Intel’s Gaudi 3 announcement provides those details.
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Intel named Dell, HPE, Lenovo and Supermicro as OEMs expected to bring Gaudi 3 systems to market. In May 2025, Intel also described a Dell AI platform using Gaudi 3, including an eight-accelerator server configuration. These examples show the OEM route to deployment; current availability depends on the system and region. Intel’s May 2025 availability announcement covers the named partners.
Intel’s AI role also includes CPUs and the surrounding system
Accelerators need host processors and infrastructure around them. Intel’s Q2 2026 update described rack-scale and disaggregated inference solutions built on Xeon processors, alongside the launch of Xeon 6+. That is part of why Intel’s data-center AI position cannot be assessed by counting accelerator products alone. Intel’s Q2 2026 earnings release discusses these products and its segment results.
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- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
What does Marvell make for AI data centers?
Marvell’s custom-silicon business works on ASICs and XPU designs tailored to customer specifications. Its platform IP and components can support compute, memory, packaging and system connectivity. In its fiscal 2025 annual filing, Marvell described high-speed SerDes, Arm compute, security, silicon photonics, chiplet and die-to-die technologies, co-packaged optics and custom HBM approaches. That filing said Marvell had completed multiple 5-nanometer designs, was progressing through 3-nanometer designs and was developing a 2-nanometer platform; those statements describe the status reported in that filing, not a guarantee of the company’s current process roadmap. Marvell’s fiscal 2025 Form 10-K describes the platform.
Custom compute is paired with the links around it
A Marvell custom accelerator package can combine XPU compute silicon, high-bandwidth memory, other chiplets and silicon-photonics engines. Beyond custom compute, Marvell lists technologies such as SerDes and die-to-die IP, PCIe retimers, CXL devices, active electrical and optical cable DSPs, PAM optical DSPs, coherent DSPs and data-center interconnect modules. These products address how data moves within and between systems as well as how compute is assembled. Marvell’s announcement of its co-packaged-optics architecture describes one such package; its bandwidth and power comparisons are company claims about a component, not a direct measure of full-system AI performance. Marvell’s June 2025 announcement explains the design.
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- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
In a corrected May 2025 release, Marvell said it was collaborating with all four top hyperscalers on custom XPUs and CPUs, as well as network-interface controllers, CXL controllers and other infrastructure devices. The statement did not identify the hyperscalers by name. It supports the distinction between Marvell’s customer-specific design work and a standard, branded accelerator sold as a general-purpose product. Marvell’s corrected release gives the company’s description of those collaborations.
How large are the businesses? Read the figures in context
The figures below describe different periods and business groupings. They show exposure to data-center demand, but they do not provide a like-for-like comparison of AI-chip sales.
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- 48GB AI graphics accelerator
| Measure | Reported figure | What it covers |
|---|---|---|
| Intel DCAI revenue, FY2025 | $16.9 billion, up 5% from FY2024 | Intel’s DCAI segment, which includes servers and networking as well as AI accelerators and other products. Intel FY2025 results. |
| Intel DCAI revenue, Q2 2026 | $6.3 billion, up 59% year over year | Quarterly DCAI segment revenue; it is not accelerator-only revenue. Intel’s release notes that segment revenue includes intersegment transactions. Intel Q2 2026 earnings release. |
| Marvell data-center share, FY2026 | Approximately three-quarters of total revenue; more than $6 billion in data-center revenue | Marvell’s May 2026 proxy statement reports the company’s FY2026 data-center exposure. Marvell’s 2026 proxy statement. |
| Marvell custom silicon, FY2026 | About 25% of data-center revenue | A share of Marvell’s data-center business, not a standalone AI-chip revenue figure. Marvell’s 2026 proxy statement. |
| Marvell optical interconnect, FY2026 | Roughly half of data-center revenue | A share of Marvell’s data-center business, not a measure of AI compute sales. Marvell’s 2026 proxy statement. |
Intel’s FY2025 DCAI total and Marvell’s FY2026 data-center measures differ both in period and in definition. Marvell’s custom-silicon and optical-interconnect shares are components of its own data-center business; they are not directly comparable with all of Intel DCAI. The reviewed company disclosures do not establish a clean standalone Intel AI-accelerator revenue figure or an equivalent Marvell AI-chip revenue figure.
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Intel’s 2025 filing also reported Gaudi-related inventory charges in DCAI: it identified $922 million in Gaudi AI-accelerator inventory-related charges recognized in 2024 and said 2025 DCAI operating income benefited from lower such charges compared with 2024. That history is relevant context when evaluating Gaudi’s financial trajectory, but it does not by itself establish current demand. Intel’s FY2025 Form 10-K contains the filing disclosures.
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- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
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Intel’s Gaudi 3 launch announcement included projected comparisons with Nvidia H100 and H200 for specified model sets and workloads. Intel’s May 2025 Dell announcement reported 70% better inference price-performance for a particular Llama 3 80B configuration, with test-data and pricing caveats. Neither claim establishes a universal winner across AI workloads. Marvell’s published bandwidth and power comparisons for its 6.4T silicon-photonics engine likewise concern a component, not end-to-end AI-system performance.
A useful comparison requires a defined workload and full system context. For any claimed result, check:
- Workload and model: training, fine-tuning, inference or RAG, and the model and model size used.
- System configuration: accelerator count, host CPU, memory, networking and software versions.
- Metric: throughput, latency, power, price-performance or another named outcome.
- Evidence and availability: whether the result is a vendor projection, a vendor-cited analysis or an independent test, and whether the compared system can be deployed in the reader’s intended setting.
Which company is the better fit?
The relevant choice depends on whether a buyer needs a defined product platform or is building a differentiated system with custom silicon. Intel’s Gaudi and Xeon products, together with OEM server configurations, are the more direct route to evaluating named Intel hardware. Marvell’s custom XPU work is relevant to hyperscalers and other customers designing their own compute platforms; its interconnect portfolio matters when scaling links, packaging and data movement. These are different roles in the AI infrastructure stack, so a single performance ranking would obscure the buying decision.
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