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Nvidia is the much larger business in the cited reporting periods, but the available figures are from different fiscal years and do not establish which company makes the better AI accelerator. AMD’s MI355X is a single data-center GPU with vendor-listed memory specifications; Nvidia’s announced Vera Rubin is a six-chip, rack-scale platform. Those are different comparison units. For investors and technology buyers alike, the useful question is not simply which name is ahead, but how each company’s reported business, product design and evidence fit the comparison being made.
How large are Nvidia and AMD’s AI businesses?
The revenue figures show a substantial difference in scale, but they are not a same-period comparison: Nvidia’s numbers are for fiscal 2026, while AMD’s are for fiscal 2025. Revenue also does not, by itself, establish profitability, valuation or the attractiveness of either company’s stock.
| Company and reporting period | Total revenue | Data-center revenue | Reporting note |
|---|---|---|---|
| Nvidia, fiscal 2026 | $215.9 billion | $193.7 billion | Company-reported results for fiscal 2026. Nvidia fiscal 2026 results |
| AMD, fiscal 2025 | $34.6 billion | $16.6 billion | Company-reported results for fiscal 2025; AMD says it combined Client and Gaming into one reportable segment beginning in FY2025. AMD fiscal 2025 annual report |
These figures indicate that Nvidia’s business was far larger in the periods shown. They do not provide a synchronized year-over-year comparison, a like-for-like breakdown of every business segment, or a direct measure of AI-chip market share. The fiscal-year labels matter, particularly when using the numbers to assess growth or compare company performance.
What products are being compared?
AMD’s Instinct MI350 series is a family of data-center GPUs for AI and high-performance computing. Nvidia’s Vera Rubin announcement describes a system platform that combines multiple chips and networking components. A single GPU and a rack-scale platform serve different comparison purposes.
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- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations.
- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
- [DisplayPort 2.1] Achieve unparalleled visual clarity and performance, driving high resolution displays at up to 8K at 240 Hz and 16K at 60 Hz. Increased bandwidth enables seamless multi-monitor setups while HDR and higher color depth support ensures superior color accuracy for precision work, such as video editing, 3D design, and live broadcasting.
- [Universal MIG] Divide a single RTX PRO 6000 Blackwell into multiple isolated instances, each with dedicated resources, allowing for concurrent execution of multiple workloads, optimized GPU utilization, and secure isolation of different applications or users. [WARRANTY] 3 YR Manufacturer's Warranty. Bulk OEM Packaging. Retail Packaging is NOT included.
| Example | Comparison unit | What the cited source establishes |
|---|---|---|
| AMD Instinct MI355X | GPU accelerator | AMD lists 288 GB of HBM3E memory and 8 TB/s memory bandwidth; the product page gives a launch date of June 12, 2025. These are vendor specifications, not comparative workload results. AMD MI355X specifications |
| Nvidia Vera Rubin | Six-chip, rack-scale platform | Nvidia describes Vera CPU, Rubin GPU, NVLink switch, ConnectX SuperNIC, BlueField DPU and Spectrum Ethernet switch. Its announcement includes platform-level performance and token-cost claims, which are Nvidia’s claims rather than independent findings. Nvidia Vera Rubin announcement |
For a chip-level comparison, MI355X should be set against an Nvidia accelerator using equivalent specifications and workload tests. For a system-level comparison, the relevant unit is a complete, comparably configured system, including interconnect and networking—not one MI355X against an entire Vera Rubin rack design.
Do the published specifications prove which GPU is faster?
No. Peak theoretical performance and memory specifications describe capabilities under defined conditions; they do not prove which accelerator will finish a particular training or inference job faster, use less energy, or cost less per result.
AMD’s MI350 product page publishes theoretical peak comparisons against Nvidia B200. AMD says its calculations were made by AMD Performance Labs in May 2025 and cautions that results can vary with server configuration, datatype and workload. That makes the figures useful as vendor-provided specification context, not as an independent, matched test or a universal ranking. AMD MI350 series and comparison methodology
Rank #2
- 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
Nvidia CEO Jensen Huang said in Nvidia’s February 25, 2026 fiscal-results release that Grace Blackwell with NVLink was “the king of inference today” and claimed an order-of-magnitude lower cost per token, adding that Vera Rubin would extend that leadership. This is an executive statement about Nvidia’s products, not an independently established comparison across workloads.
Is AMD catching up to Nvidia in AI?
There is evidence that AMD’s data-center accelerators are being deployed, but the cited information does not establish how its overall AI infrastructure share compares with Nvidia’s. AMD’s annual report says large hyperscale customers, OEMs and ODMs deployed MI350X systems, and that Meta and Oracle expanded availability of MI350-based infrastructure. Nvidia named AWS, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure as planned early deployers of Vera Rubin. These are company-reported deployment statements; they are not directly comparable counts of systems, capacity, revenue or market share.
They show that both companies are pursuing large-scale infrastructure deployments, while leaving the size and relative share of those deployments unsettled. “Catching up” therefore depends on the measure: product capability, customer adoption, available compute, revenue, or performance on a specific workload. The cited announcements alone do not answer all of those questions.
Rank #3
- Professional GPU with Blackwell Architecture
- Blackwell Architecture
- 24GB GDDR7 with PCIe 5.0 & Ray Tracing
- AI Workstation
What should a buyer or investor compare next?
A useful evaluation starts with the job to be done and the evidence for it. Compare the same workload on systems configured as closely as possible; a peak number from one vendor and a system-level claim from another are not an apples-to-apples test.
- Workload and benchmark provenance: Match training or inference tasks, model, dataset, software version and test conditions. Check whether results come from a vendor, a customer or an independent test.
- Precision and memory: Verify the datatype used for performance claims, along with capacity and bandwidth needs for the model and batch size. A specification in one precision does not establish performance in another.
- Whole-system design: Include accelerator count, memory configuration, CPU, networking and interconnect. Rack-scale platform claims should be compared with a complete competing system.
- Software and migration: Check compatibility with the buyer’s frameworks, libraries, deployment tools and existing code. The cited sources do not provide a neutral, current CUDA-versus-ROCm compatibility or migration assessment.
- Power and total cost: Compare measured energy and cost for the same completed workload, including system and operating costs. The cited material does not establish neutral, comparable transaction prices, power-to-performance results or current regional availability.
For investors, add the financial questions that chip specifications cannot answer: margins, capital needs, competition, customer concentration, valuation and the durability of demand. The revenue figures above establish business scale for their stated fiscal periods, not which stock is the better investment.
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