Nvidia GPU Roadmap: Blackwell, Rubin, Feynman and What Comes Next

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Nvidia does not have one single GPU roadmap. Its most detailed public roadmap covers data-center AI platforms: Blackwell, Blackwell Ultra, Rubin, Rubin Ultra and Feynman. For gaming, the confirmed consumer generation remains the Blackwell-based GeForce RTX 50 series. Nvidia has not officially announced an RTX 60-series lineup, launch date or confirmed connection between RTX 60 and Rubin.

As of August 10, 2026, Rubin is in production and is being deployed by early cloud and infrastructure partners, with broader partner availability targeted for the second half of 2026. Rubin Ultra remains a 2027 roadmap target, while Feynman is the next named major architecture, generally associated with an approximately 2028 target—but its commercial schedule and specifications are not yet firm.

Nvidia GPU roadmap at a glance

The answer depends on which Nvidia product family you mean. A data-center accelerator, a GeForce graphics card, an RTX PRO workstation card and an RTX Spark personal-AI computer may use related technology, but they are not interchangeable products and do not follow an identical launch schedule.

Generation or platform	Public timing	Main products or systems	Status as of August 2026
Blackwell	2024 onward	B200, GB200, GB200 NVL72; GeForce RTX 50	Current prior-generation data-center platform and current confirmed GeForce architecture
Blackwell Ultra	Second half of 2025	B300, GB300, GB300 NVL72	Shipping and ramping in data centers
Rubin / Vera Rubin	Second half of 2026	Rubin GPU, Vera CPU, Vera Rubin NVL72	In production, ramping and deploying at early partners
Rubin Ultra	2027 target	NVL144, NVL576 and Kyber-based systems	Public roadmap target; infrastructure timing has execution risk
Feynman / Rosa	Approximately 2028 target	Feynman GPU, Rosa CPU and a future full-stack platform	Named and partly described; commercial timing and specifications remain unconfirmed

Nvidia’s data-center sequence is therefore:

Blackwell → Blackwell Ultra → Rubin → Rubin Ultra → Feynman

That sequence should not be copied directly onto the GeForce product line. The existence of a Rubin data-center platform does not prove that Nvidia will release a Rubin-based GeForce RTX 60 card.

What is Nvidia’s latest GPU architecture?

The most recently named major Nvidia architecture is Feynman, which Nvidia introduced as the successor to Vera Rubin at GTC 2026. The newest platform entering real-world deployment, however, is Rubin. For consumer gaming, the latest officially announced generation is still Blackwell-based GeForce RTX 50.

Those three answers are all correct because they refer to different markets:

• Latest named data-center architecture: Feynman.
• Newest data-center platform entering deployment: Rubin/Vera Rubin.
• Latest confirmed GeForce gaming architecture: Blackwell, used by the RTX 50 series.

Nvidia announced its original Blackwell data-center architecture on March 18, 2024, its GeForce RTX 50 series on January 6, 2025, and Blackwell Ultra at GTC 2025. The company later expanded its public roadmap with Rubin and Feynman announcements. The official Blackwell announcements cover the data-center platform and the consumer GeForce RTX 50 family.

Blackwell: the foundation of the current generation

Blackwell is both a data-center architecture and a consumer graphics architecture, but the products built from it are substantially different.

Data-center Blackwell

The original data-center Blackwell family includes the B200 GPU, the GB200 Grace Blackwell superchip and rack-scale systems such as GB200 NVL72. Nvidia disclosed 208 billion transistors, two reticle-sized dies connected by a high-bandwidth interface, fifth-generation NVLink, a second-generation Transformer Engine and support for low-precision AI formats including FP4.

A GB200 NVL72 rack combines:

• 72 Blackwell GPUs.
• 36 Grace CPUs.
• A fifth-generation NVLink fabric.
• Liquid cooling.
• Rack-scale software and hardware that allow the rack to operate as a unified accelerator.

This is not the same type of product as a GeForce card installed in a desktop. It is a high-density AI system intended for large-scale model training and inference. Nvidia’s original Blackwell announcement provides the architecture and GB200 NVL72 details.

GeForce RTX 50: consumer Blackwell

The GeForce RTX 50 series is also based on Blackwell, but it is designed for gaming, content creation and local AI rather than rack-scale data-center operation. Nvidia announced desktop and laptop RTX 50 products, including the RTX 5090, with fifth-generation Tensor Cores, fourth-generation RT Cores, neural-rendering features and DLSS 4.

Data-center Blackwell and GeForce Blackwell are related architectural families, not identical products. They differ in:

• Memory technology and capacity.
• Chip packaging and interconnects.
• Power limits and cooling requirements.
• Form factors.
• Drivers and software targets.
• Validation, pricing and deployment models.

Consequently, a data-center Blackwell specification cannot be used to predict the specifications of a GeForce RTX 50 or a future consumer card.

Blackwell Ultra: the enhanced 2025 platform

Blackwell Ultra is Nvidia’s enhanced data-center generation between original Blackwell and Rubin. Nvidia announced it at GTC on March 18, 2025, with partner availability beginning in the second half of 2025.

The main systems are:

• GB300 NVL72: 72 Blackwell Ultra GPUs and 36 Grace CPUs.
• HGX B300 NVL16: a more conventional server configuration with 16 Blackwell Ultra GPUs.

Nvidia claims that GB300 NVL72 delivers approximately 1.5 times the AI performance of GB200 NVL72. The company also positions Blackwell Ultra for reasoning models, test-time scaling, agentic AI and physical AI. That 1.5-times figure is a vendor claim, and it should not be read as a universal speed increase in every application. Results depend on the model, precision, software, batch size, networking and power configuration. Nvidia’s Blackwell Ultra announcement describes the products and claimed improvements.

Blackwell Ultra also shows why Nvidia’s data-center roadmap is not simply a new architecture every two years. The company is using an alternating pattern:

1. A major architecture, such as Blackwell.
2. An enhanced or “Ultra” generation with additional system-level improvements.
3. The next major architecture, such as Rubin.

An Ultra product can change memory, networking, packaging, rack design and AI capabilities while retaining substantial compatibility with the preceding architecture. Calling it merely a clock-speed refresh would understate its role.

Rubin and Vera Rubin: Nvidia’s 2026 AI platform

Rubin timing and availability

Nvidia first presented Rubin as the successor to Blackwell at GTC 2025. The company later said Rubin-based products would be available from partners in the second half of 2026.

Nvidia said at CES 2026 that the Rubin platform was in full production. In March 2026, it described seven chip types as being in full production. By July 2026, Nvidia said Vera Rubin NVL72 systems were ramping and operating at or being deployed by CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure and Nebius. The relevant announcements are Nvidia’s Rubin platform overview, its Vera Rubin platform announcement and its July deployment update.

These status terms are important:

Term	What it means	What it does not necessarily mean
Announced	Nvidia has publicly described the product or platform.	That customers can immediately obtain it.
In production	Nvidia says the silicon or systems are being manufactured.	Broad retail, cloud or regional availability.
Ramping	Manufacturing and partner deployment are increasing.	Unlimited supply or general availability.
Available from partners	Selected OEMs, cloud providers or infrastructure partners can provide systems or capacity.	That every region or customer can buy one.
Generally available	Commercial access is broadly offered, subject to provider, region and supply.	That it is a retail graphics card.

Therefore, “Rubin is in production” does not mean that consumers can buy a Rubin GeForce graphics card. It refers to Nvidia’s data-center platform and its manufacturing and deployment status.

Rubin GPU specifications

Nvidia’s July 2026 technical description lists the following specifications for the Rubin GPU:

• 336 billion transistors.
• 224 streaming multiprocessors.
• 896 Tensor Cores.
• Up to 50 petaflops of NVFP4 inference performance.
• Up to 288 GB of HBM4.
• Up to 22 TB/s of memory bandwidth.
• NVLink 6 with up to 3,600 GB/s of scale-up bandwidth.
• PCIe Gen 6 host connectivity.

These are Nvidia’s published Rubin GPU figures, not specifications for every future Rubin product configuration. The company’s Rubin architecture technical overview also compares approximately 22 TB/s of Rubin memory bandwidth with about 8 TB/s for Blackwell and Blackwell Ultra.

The bandwidth increase matters because many modern AI workloads are limited by moving data rather than by theoretical arithmetic throughput. Autoregressive inference, for example, repeatedly accesses model weights and the key-value cache used to retain context. Higher bandwidth can help those workloads, although actual performance still depends on model structure, sequence length, precision, software and utilization.

How to interpret Rubin’s AI performance numbers

Rubin’s headline numbers require careful interpretation:

• NVFP4 is not FP16, BF16 or FP8. A result measured in NVFP4 cannot be compared directly with a result measured in another precision.
• “Up to” is not an everyday performance guarantee. It identifies a best-case or selected workload result.
• One GPU is not the same as one NVL72 rack. Rack-level results include NVLink, CPUs, networking, software and multiple GPUs.
• Tokens per second is not the same as model quality. A faster low-precision result may require an accuracy or model-quality comparison.
• Cost per token includes more than GPU throughput. Power, cooling, networking, utilization, software licensing, system cost and cloud pricing all matter.

Nvidia claims that Rubin can reduce inference token cost by up to 10 times and reduce the number of GPUs required to train certain mixture-of-experts models by up to four times compared with Blackwell. These are Nvidia platform claims tied to stated comparisons; they are not guaranteed results for every model or deployment.

Vera Rubin is a complete platform, not just a GPU

“Rubin” can refer to the GPU architecture, while Vera Rubin generally refers to the larger platform built around it. Nvidia’s platform includes:

• Vera CPU.
• Rubin GPU.
• NVLink 6 switch.
• ConnectX-9 SuperNIC.
• BlueField-4 DPU.
• Spectrum-6 Ethernet switch.
• Groq 3 LPU integration in the later 2026 platform description.

Early January 2026 material described Rubin as a six-chip platform. Later GTC 2026 material described seven chip types after Nvidia integrated Groq 3 LPU technology. The changing count reflects the evolution of the platform description rather than two different Rubin generations. Nvidia’s earlier six-chip explanation and later Vera Rubin announcement show that progression.

Vera Rubin NVL72

The flagship Rubin system is Vera Rubin NVL72, which combines:

• 72 Rubin GPUs.
• 36 Vera CPUs.
• NVLink 6 scale-up networking.
• ConnectX-9 and Spectrum-6 networking.
• BlueField-4 DPUs.
• Liquid cooling.

Nvidia’s platform description also cites 2 TB of fast memory per compute tray and 200 petaflops of NVFP4 AI performance per compute tray. These are system-level figures in Nvidia’s platform description, not a claim that a single Rubin GPU delivers 200 petaflops.

The infrastructure is important. NVLink is used for scale-up: connecting processors inside a tightly coupled system so that they can exchange data with very high bandwidth and low latency. Ethernet and related networking components are used primarily for scale-out: connecting separate servers, racks and storage resources. A Rubin deployment therefore requires coordinated CPUs, GPUs, switches, DPUs, NICs, software, storage and cooling rather than a simple accelerator-card replacement.

Evidence of Rubin deployment

By July 21, 2026, Nvidia said Vera Rubin NVL72 systems were running at or being deployed by:

• CoreWeave.
• Google Cloud.
• Microsoft Azure.
• Oracle Cloud Infrastructure.
• Nebius.

Nvidia also highlighted a CoreWeave DeepSeek-R1 benchmark claiming 10 times more tokens per second per megawatt than Grace Blackwell NVL72. This should be treated as a partner-reported result under a particular test configuration, not as an independently verified industry-wide conclusion. The comparison can change with model, batch size, precision, software version, power limits and system utilization.

Rubin Ultra and Kyber: the 2027 target

Rubin Ultra is the enhanced platform planned after Rubin. Nvidia’s public roadmap places Rubin Ultra systems in 2027.

The largest disclosed configuration is Vera Rubin Ultra NVL576:

• Eight NVL racks.
• 72 Rubin Ultra GPU packages per rack.
• 576 GPUs in one NVLink domain.
• Copper and optical interconnects.
• A common MGX rack-scale ecosystem.

Nvidia has also described Kyber, a next-generation rack design intended to support a 144-GPU NVLink domain. Kyber is expected to debut with Rubin Ultra as an NVL144 system and later provide a foundation for larger Feynman systems. More detail is available in Nvidia’s Vera Rubin pod and rack-scale overview and its GTC 2026 session.

Is Rubin Ultra delayed?

There is a schedule risk, but no confirmed cancellation or official revised launch date. In July 2026, reports suggested that the Kyber rack architecture could slip toward 2028. Nvidia responded that “our roadmap is intact,” but did not publish a detailed revised production schedule.

The accurate description is therefore:

Nvidia’s public target remains 2027 for Rubin Ultra systems, but reported Kyber infrastructure delays create execution risk. Nvidia says its roadmap remains intact; a revised official shipping schedule has not been published.

That distinction matters because large AI systems depend on many components arriving together. Even if Rubin Ultra silicon is ready, rack design, optical links, cooling, networking, validation and customer-facility readiness can affect when a complete system reaches production.

Feynman and Rosa: the next major generation

At GTC 2026, Nvidia identified Feynman as the major architecture after Vera Rubin. The public platform description includes:

• A new Feynman GPU.
• The Rosa CPU.
• LP40, a next-generation low-latency processor.
• BlueField-5.
• CX10 networking.
• Kyber scale-up infrastructure.
• Greater use of optical interconnects.

Nvidia’s description continues its strategy of treating compute, memory, storage, networking, security and cooling as one co-designed system. The company’s GTC 2026 announcements provide the current public description of Feynman and its associated platform components.

Third-party reporting of Nvidia’s earlier roadmap commonly placed Feynman around 2028. That remains a useful planning reference, not a guaranteed commercial launch date. Nvidia’s current public material names the architecture and its surrounding technologies but does not establish final specifications, a firm retail date or a definitive product lineup.

It is not currently possible to state as fact that:

• Feynman will launch in 2028.
• Feynman specifications are finalized.
• Feynman will become a GeForce RTX 60 architecture.
• Feynman will use a particular manufacturing process.
• Feynman will have a particular HBM capacity, transistor count or power limit.

Nvidia’s corporate roadmap has described a progression from Blackwell to Rubin to Feynman, but roadmaps are plans rather than binding commitments. Product features, specifications and availability can change.

What does this mean for GeForce RTX 60?

Nvidia has not officially announced RTX 60. As of August 10, 2026, the confirmed consumer generation is the Blackwell-based GeForce RTX 50 series. Nvidia’s 2026 GeForce announcements focused on DLSS 4.5, path tracing, RTX Remix, neural rendering, local AI and software updates for existing RTX products; they did not reveal an RTX 60 lineup or launch date. See Nvidia’s GDC 2026 GeForce announcements alongside the original RTX 50 announcement.

The following claims remain unverified:

• “RTX 60 will use Rubin.”
• “RTX 60 launches in 2027.”
• “RTX 60 launches in 2028.”
• “Rubin consumer GPUs will use HBM4.”
• “The next GeForce flagship will be called RTX 6090.”
• “Feynman is the RTX 60 architecture.”

Nvidia can use different dies, memory systems, packaging, power targets, names and launch schedules for data-center and consumer products. A data-center architecture may influence future GeForce technology, but that relationship must be confirmed by Nvidia before it becomes a product fact.

RTX Spark is not the RTX 60 roadmap

DGX Spark is a personal-AI computer built around Nvidia’s Grace Blackwell technology. At Computex 2026, Nvidia publicly showed a longer-term roadmap for the broader RTX Spark personal-AI platform.

Reported coverage of the roadmap places the generations approximately as follows:

• Grace Blackwell Spark: 2026.
• Vera Rubin Spark: 2027 or 2028.
• Rosa Feynman Spark: later, with some reporting suggesting approximately 2030.

These dates describe AI-focused PCs, superchips and a partner ecosystem. They do not establish the launch date or architecture of a future discrete GeForce graphics card. The reported Computex roadmap is covered by Tom’s Hardware and PC Gamer.

RTX Spark is evidence that Nvidia intends to bring its AI-computing platforms to personal computers. It is not evidence that a Rubin-based GeForce RTX 60 card has been announced.

Professional and workstation GPUs

Nvidia’s current professional lineup is based on Blackwell and includes:

• RTX PRO 6000 Blackwell Workstation Edition.
• RTX PRO 6000 Blackwell Max-Q.
• RTX PRO 5000, 4500, 4000 and 2000 Blackwell products.
• RTX PRO 6000 Blackwell Server Edition.

Nvidia has not published a detailed future RTX PRO roadmap equivalent to its data-center AI roadmap. Professional buyers should not assume that the next RTX PRO generation will follow the same timing or naming as GeForce, B-series data-center GPUs or Rubin. RTX PRO products have different drivers, validation requirements, memory systems, form factors and software support. Nvidia’s RTX PRO Blackwell announcement describes the current professional family.

How the different Nvidia product categories fit together

Product category	Typical purpose	Current roadmap certainty
GeForce RTX	Gaming, creative applications and consumer AI	RTX 50/Blackwell confirmed; RTX 60 not officially announced
RTX PRO	Workstations, professional visualization, engineering and certified applications	Current family is RTX PRO Blackwell; detailed next-generation roadmap not public
B-series and GB-series data-center GPUs	AI training, inference and high-performance computing	Detailed Blackwell, Blackwell Ultra and Rubin roadmap
DGX and RTX Spark	Integrated AI systems for enterprises, developers and personal AI	Future Rubin and Rosa/Feynman-related platforms disclosed, but not a GeForce schedule

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Technical terms that explain the roadmap

Architecture versus product family

An architecture is the underlying GPU design and computing technology. A product family is a set of products built for a market. Blackwell is an architecture used in both data-center and GeForce products, while B200, GB200, RTX 5090 and GB300 are specific products or systems with different implementations.

GPU die versus GPU package

A GPU die is the silicon containing the processing hardware. A package may combine one or more dies with high-bandwidth memory, interconnects and other components. Therefore, “72 GPUs” in an NVL72 system is a system count, while a transistor count or HBM figure may describe an individual GPU package. These numbers should not be mixed.

Discrete GPU versus superchip

A discrete GPU is an accelerator that can be installed in a server or PC. A superchip combines a CPU and GPU, often with a high-bandwidth connection between them. GB200 and Vera Rubin are examples of platform designs that combine CPU and GPU resources rather than representing ordinary standalone graphics cards.

Rack-scale system versus server card

A rack-scale system such as GB200 NVL72 or Vera Rubin NVL72 contains dozens of GPUs, CPUs, switches, networking devices, power delivery systems and cooling equipment. It may be managed as a unified accelerator, but it cannot be compared directly with a single GeForce card using one headline FLOPS number.

HBM capacity versus HBM bandwidth

HBM capacity is how much high-bandwidth memory is available for model weights, activations and caches. HBM bandwidth is how quickly data can move between the memory and the GPU. More capacity can allow a larger model or longer context to fit; more bandwidth can help the GPU process memory-heavy workloads faster. Capacity and bandwidth solve different bottlenecks.

FP4, NVFP4, FP8, FP16 and BF16

These are numerical formats with different precision and storage requirements. Lower-precision formats can greatly increase AI throughput and reduce memory use, but they may require software support, quantization and accuracy validation. NVFP4 results should not be compared directly with FP16, BF16 or FP8 results. A higher petaflop number in a lower-precision mode does not automatically mean a proportionally faster or more accurate application.

Training versus inference

Training adjusts a model’s parameters and is usually highly parallel and computationally demanding. Inference runs an already trained model. Inference includes the initial prompt or context-processing stage, often called prefill, and the step-by-step generation stage, often called decode or token generation.

Prefill can be dominated by large matrix operations, while decode can be more sensitive to memory bandwidth, latency, KV-cache access and communication between GPUs. Rubin’s memory and interconnect improvements may therefore be particularly important for long-context inference, mixture-of-experts routing and agentic workloads, but not every local AI task will benefit equally.

GPU count versus usable model capacity

Adding GPUs does not simply multiply usable model capacity or application speed. Models may need to be partitioned across GPUs, and the result depends on memory distribution, interconnect bandwidth, parallelism strategy, communication overhead and software. A 72-GPU rack can behave very differently from 72 unrelated PCIe cards.

Should you buy an Nvidia GPU now or wait?

For gamers

Buy now if you need a gaming upgrade, your target resolution and games justify an RTX 50-series card, and current driver and game support matter more than unannounced hardware.

Wait if your current GPU is adequate, the purchase is discretionary and you are comfortable with an unknown future launch schedule, possible pricing changes and possible supply constraints. Waiting for “Rubin” specifically is not a date-based recommendation because Nvidia has not confirmed Rubin as a GeForce architecture.

For a gaming purchase, prioritize:

• Performance in the games you actually play.
• Resolution, refresh rate and ray-tracing requirements.
• VRAM capacity and expected game longevity.
• Power consumption, connector requirements and case space.
• DLSS, frame-generation and other software features you will use.
• Actual street price and availability rather than roadmap rumors.

For local AI users

Do not choose solely by architecture name or theoretical AI throughput. Check VRAM capacity, supported precisions, CUDA and framework compatibility, model quantization support, software maturity, power draw and whether the model fits on one GPU. A future Rubin Spark system may be more relevant to some personal-AI users than a future GeForce card, but its final products, prices and regional availability must be verified when announced.

For AI developers

The relevant questions include:

• How much VRAM does the model require?
• Is the workload training, prefill, decode or a mixture?
• Which precision formats preserve the required quality?
• Does the application need NVLink or multi-GPU model parallelism?
• Can the required Rubin capacity be rented from a cloud provider?
• What are the software, compiler and kernel support timelines?
• What is the cost per useful token at the required quality and latency?
• Would a smaller current system be more economical than waiting?

Rubin’s advantages are most likely to matter for large models, long contexts, mixture-of-experts systems, agentic workloads and high-volume inference. They may be much less important for a small model that already runs comfortably on a single current GPU.

For data-center operators

Rubin is not a drop-in replacement for a PCIe accelerator. Operators should plan for:

• Liquid cooling and high rack power density.
• NVLink 6 scale-up infrastructure.
• High-speed Ethernet and compatible networking.
• New rack, tray and power-delivery designs.
• Coordination across GPUs, CPUs, DPUs, NICs, switches and storage.
• Facility capacity and cooling upgrades.
• Software validation and model-parallel deployment.

Nvidia’s Rubin materials emphasize rack, network, storage and cooling co-design. The purchase decision is therefore about a complete AI factory or cluster, not just the price of an accelerator.

Confirmed versus unconfirmed roadmap information

Confirmed or publicly stated	Not confirmed
Rubin systems targeted for partner availability in the second half of 2026	A retail Rubin GeForce graphics card
Nvidia has described Rubin as in production, ramping and deploying at early partners	An RTX 60 launch date
Rubin GPU figures including up to 288 GB HBM4 and 22 TB/s bandwidth in Nvidia’s technical description	That every Rubin product will use those exact specifications
Rubin Ultra remains a 2027 public roadmap target	A guaranteed Rubin Ultra shipping date
Kyber is intended to support a 144-GPU NVLink domain and larger systems	That reported Kyber delays have become an official cancellation or confirmed 2028 delay
Feynman, Rosa, LP40, BlueField-5 and CX10 have been publicly named or described	Final Feynman specifications, pricing or commercial launch date
Future RTX Spark generations using Rubin and later Rosa/Feynman technology have been shown in reported Computex coverage	That RTX Spark dates establish the GeForce RTX 60 schedule
The current professional family is RTX PRO Blackwell	A detailed future RTX PRO product roadmap

The practical conclusion for each audience

• Gamers: RTX 50 is the latest confirmed GeForce generation. Buy based on current performance and price, or wait without assuming that Rubin is an announced gaming product.
• Workstation users: The current professional family is RTX PRO Blackwell. Treat future RTX PRO rumors separately from GeForce and data-center announcements.
• Local AI users: Compare VRAM, supported precision, software and power—not just architecture names.
• Cloud AI developers: Rubin availability may arrive through selected providers before it becomes broadly accessible everywhere. Evaluate workload economics rather than peak headline numbers.
• Data-center operators: Rubin requires platform-level planning for liquid cooling, networking, rack density and software integration.

Frequently Asked Questions

Can consumers buy a Rubin GPU?

Rubin is primarily a data-center AI platform. As of August 10, 2026, Nvidia says Rubin systems are in production and deploying through early partners, but it has not announced a consumer Rubin GeForce graphics card or retail availability.

Will RTX 60 use Rubin?

Nvidia has not confirmed the architecture, branding or launch date of RTX 60. Rubin is a data-center roadmap name, so it should not be treated as the confirmed architecture for a future GeForce card.

Is Rubin a GPU or a complete computer system?

Both terms can be used at different levels. Rubin refers to Nvidia’s GPU architecture, while Vera Rubin refers to a platform combining Rubin GPUs with Vera CPUs, NVLink 6, networking, DPUs and rack-scale infrastructure.

What is the difference between Rubin and Rubin Ultra?

Rubin is the major data-center generation targeted for partner availability in the second half of 2026. Rubin Ultra is the enhanced generation that follows it, with a public 2027 target and larger NVL144 and NVL576 rack-scale configurations.

The Bottom Line

Bottom line: Nvidia’s data-center roadmap is clear through Rubin and increasingly detailed into Rubin Ultra and Feynman. Its GeForce roadmap is not. RTX 50 remains the latest confirmed consumer generation; RTX 60, Rubin GeForce and Feynman GeForce are unconfirmed. Buy current hardware when you need its performance, and treat future-generation waiting as a speculative choice rather than a schedule-backed plan.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.