PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAt CES on January 5, 2026, NVIDIA did not unveil a major new consumer GeForce GPU generation or RTX 50-series refresh. Instead, CEO Jensen Huang made Rubin—the company’s next-generation, data-center-focused AI computing platform—the hardware centerpiece. NVIDIA did not skip GPUs altogether: Rubin includes a new GPU, but it is not a retail gaming card.
For gamers, the event brought software and rendering news rather than the new GeForce hardware some expected. For cloud providers and businesses weighing AI infrastructure, the headline was a coordinated system of processors, networking and software, with partner availability expected in the second half of 2026.
What NVIDIA did—and did not—announce for gaming
The distinction is between a consumer GPU launch and an AI platform announcement. NVIDIA did not use CES 2026 to reveal a new mainstream GeForce generation or a major RTX 50-series refresh. It continued to discuss gaming-related software, including DLSS developments, and the event coverage described a focus on neural rendering. That is not the same as introducing a new gaming card. Tom’s Hardware’s CES keynote coverage and its Day Zero roundup document the software and gaming context.
That omission stood out because CES is a prominent PC-hardware launch window and NVIDIA’s GeForce products are highly visible to consumers. It does not establish that NVIDIA is leaving gaming or that it will not announce consumer hardware elsewhere. It does show that, at this CES, the company chose to put AI infrastructure ahead of a new GeForce product reveal.
Free tools Windows power users keep installed
One-click scans. No signup required.
#1 Best Overall
- AI Performance: 767 AI TOPS
- OC mode: 2632 MHz (OC mode)/ 2602 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- A 2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis
What Rubin is: an AI supercomputer platform, not a GeForce card
NVIDIA described Rubin as its first “extreme-codesigned” six-chip AI platform. In practical terms, the company is presenting a coordinated system for building and operating large AI clusters—not simply a faster accelerator that can be considered in isolation. The initial CES configuration included these components:
- Vera CPU: host and general-purpose compute for the system.
- Rubin GPU: accelerated compute aimed at AI workloads.
- NVLink 6 Switch: high-bandwidth communication among GPUs.
- ConnectX-9 SuperNIC: networking for communication across a cluster.
- BlueField-4 DPU: infrastructure, security and data-processing functions.
- Spectrum-6 Ethernet Switch: high-performance Ethernet networking.
The component list and system explanation are set out in NVIDIA’s technical overview and the company’s Rubin announcement. The Vera CPU is a platform component, not a consumer desktop CPU announcement.
Rank #2
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5060
- Integrated with 8GB GDDR7 128bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
The strategy is to design compute, data movement and networking together. Large AI workloads can be constrained not just by accelerator arithmetic, but by memory capacity and bandwidth, GPU-to-GPU communication, scale-out networking, power, cooling, storage and software orchestration. Mixture-of-experts models and long-context or agentic inference can make those system-level demands especially important. NVIDIA calls its integrated approach “extreme codesign”; that is the company’s description, not an independent evaluation of the platform.
How to interpret NVIDIA’s Rubin performance claims
NVIDIA announced several headline figures, all of which need their comparison and workload context. They are company claims, not independently verified results in the cited announcements.
Rank #3
- Powered by the NVIDIA Blackwell architecture and DLSS 4. System Requirements: Minimum 850W PSU with 16-pin 12V-2x6 (12VHPWR) connector required. Verify before purchasing.
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability. Compatibility: 348mm (13.7") length, 3.6 slots, 4.3 lbs. Confirm case clearance and slot spacing. GPU bracket included.
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.6-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
| Claim | What NVIDIA says it measures | What it does not establish |
|---|---|---|
| Up to 10× lower inference token cost versus Blackwell | NVIDIA’s stated comparison of token-generation costs for inference. | It is not a claim that every application runs 10× faster or costs 10× less. Actual cost depends on model, utilization, software, system configuration, power and commercial terms. |
| Up to 4× fewer GPUs for mixture-of-experts training versus Blackwell | NVIDIA’s stated GPU-count comparison for training MoE models. | It does not mean every training job needs one-quarter as many accelerators, or that total system cost and complexity fall by the same ratio. |
| 50 petaflops of NVFP4 inference compute per Rubin GPU | A company-reported figure for AI inference using the NVFP4 numerical format. | NVFP4 is an AI precision format; this is not a gaming frame-rate, rasterization or conventional gaming-GPU benchmark. |
| Up to 3.6 TB/s bandwidth per GPU | NVIDIA’s stated per-GPU bandwidth figure for Rubin. | It should not be confused with the bandwidth of an entire rack or assumed to describe every memory or workload metric. |
| Up to 260 TB/s for a Vera Rubin NVL72 rack | NVIDIA’s stated rack-level bandwidth figure. | It is a system-scale figure, not the bandwidth of one GPU, and cannot be extrapolated linearly to a different configuration. |
The 10× and 4× comparisons appear in NVIDIA’s platform announcement and its investor release. NVIDIA’s reported compute and bandwidth figures are detailed in its technical platform post. Before using these figures to forecast savings, an organization would need workload-specific results that account for model architecture, precision, sequence length, batch size, software, power and utilization.
Who should pay attention to Rubin?
AI developers
Rubin is most relevant to teams planning large-scale inference, long-context reasoning, agentic systems, mixture-of-experts training or workloads that rely on high-throughput communication across many GPUs. It is not automatically a sensible upgrade for a developer running small local models or a team whose existing cloud GPUs already meet its performance and cost needs. A local workstation and a rack-scale AI platform solve different problems.
Rank #4
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Powered by GeForce RTX 5070 Ti
- Integrated with 16GB GDDR7 256bit memory interface
- PCIe 5.0
- WINDFORCE cooling system
Cloud providers and enterprises
The useful purchasing question is not simply “Which Rubin GPU should we buy?” Rubin is being presented as an integrated platform, so buyers will need to assess the offered system configuration and service—not just a chip specification. Before committing, ask potential providers or systems partners about:
- Available rack configurations, networking and storage, and whether those match the target workload.
- Cloud regions, capacity, quotas, deployment lead times and support terms.
- Performance and cost per token on the buyer’s own models and expected utilization.
- Power, cooling, data-center capacity and operational staffing requirements.
- Software compatibility and the implications of building around NVIDIA’s CUDA and networking ecosystem.
- Total cost of ownership, including power, networking, software, financing and vendor pricing—not just accelerator count.
NVIDIA’s announcement said partner products were expected in the second half of 2026; it did not establish universal availability, a retail checkout path or public pricing. The company named cloud and infrastructure partners, including CoreWeave and Microsoft, in its platform announcement. Actual configurations, regions, capacity and prices need to be confirmed with providers when offered.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsBest Value
- AI Performance: 1005 AI TOPS
- OC mode boosts clock 2587 MHz (OC mode) / 2557 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- SFF-Ready enthusiast GeForce card compatible with small-form-factor builds
- Axial-tech fans feature a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
Gamers and PC buyers
Rubin is not a direct replacement for a GeForce RTX card. Its GPU is part of a data-center AI platform, and the announced AI performance figures are not gaming benchmarks. For a gaming PC purchase, compare consumer GeForce products actually sold for that purpose; do not wait for a Rubin retail card on the assumption that NVIDIA announced one at CES. The event’s lack of a new GeForce reveal is a change in emphasis at that event, not evidence of a gaming-market exit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Availability: production is not the same as a product on every shelf
At CES, NVIDIA said Rubin was in full production and expected partner availability in the second half of 2026. Those statements concerned AI infrastructure systems, not consumer GeForce cards. “In production” does not mean that every partner configuration is immediately orderable in every country; initial capacity and deployment schedules may vary.
Later in 2026, NVIDIA announced broader Vera Rubin platform production and an expanded platform that adds Groq 3 LPX. That is a subsequent development, separate from the six-chip Rubin configuration introduced at CES. See the later production update and the Vera Rubin platform announcement for the later configuration. Neither changes what CES announced on January 5.
Rubin was part of a wider AI ecosystem pitch
NVIDIA’s CES program extended beyond data-center hardware. The company highlighted open models and tools for healthcare, robotics and autonomous driving; Alpamayo, an open reasoning-model family for autonomous-vehicle development; physical-AI and robotics work; and NVIDIA DRIVE software, including an AI-defined Mercedes-Benz CLA demonstration. It also discussed DGX Spark and DGX Station for AI work at smaller scales, alongside BlueField-4 and AI-native storage infrastructure. The breadth of the announcements is reflected in NVIDIA’s CES presentation coverage and CES press kit.
That broader program helps explain the keynote’s emphasis: NVIDIA framed AI as infrastructure spanning cloud systems, developer machines, vehicles, robotics and other physical-world applications. Rubin was the data-center centerpiece of that story, not a renamed GeForce launch.
Quick Recap
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.
