Table of Contents
What Was Officially Confirmed in the Last 72 Hours
On March 3, 2026, NVIDIA issued a formal press release confirming the full details of GTC 2026, its annual AI conference, scheduled for March 16–19 in San Jose, California. The event will host more than 30,000 attendees from over 190 countries, with 1,000+ sessions. Jensen Huang delivers the keynote at SAP Center on March 16 at 11 a.m. PT — livestreamed free on nvidia.com. An investor Q&A is confirmed for March 17 at 9 a.m. PT. (Source: NVIDIA Newsroom, March 3, 2026)
NVIDIA’s press release states the keynote will outline advancements across “accelerated compute and AI factories to open models, agentic systems and physical AI.” Confirmed speakers include leaders from A16Z, AI2, Black Forest Labs, Cursor, Reflection AI, and Thinking Machines Lab for a discussion on frontier open models.
On chip announcements specifically: Jensen Huang stated — quoted by VideoCardz from a Korea Economic Daily report — that NVIDIA will “unveil a chip that will surprise the world” and that it has “a few new chips the world has never seen before.” NVIDIA has not identified which product this refers to, nor which silicon category. The announcement of a reveal is confirmed; the content of that reveal is not.
This is the editorial standard applied throughout this article. Every specification and timeline claim is sourced from official press releases or primary technical documentation. Where analyst projections appear, they are explicitly identified as such.
This article builds on three prior NeuralCoreTech deep-dives:
- NVIDIA Vera Rubin vs Blackwell: The AI Hardware Shift That Will Redefine 2026 — full Vera Rubin technical breakdown
- Core AI Infrastructure 2026: Hardware, Models & What Actually Runs AI — full infrastructure stack context
- Best AI Tools 2026: ChatGPT Just Hit 900M — What Happens Next? — how model demand shapes hardware decisions
What Is the NVIDIA Vera Rubin NVL72 and When Does It Ship?
NVIDIA officially launched the Vera Rubin NVL72 at CES 2026. Jensen Huang confirmed during the keynote that the platform was in full production at that time. NVIDIA confirmed on its Q4 FY2026 earnings call (February 25, 2026) that first samples had shipped to customers during that week.
NVIDIA Q4 FY2026 financial results (confirmed): Total revenue $68.1 billion; Data Center segment $62.3 billion — approximately 75% year-over-year increase; full fiscal year FY2026 revenue $215.9 billion. (Source: NVIDIA Q4 FY2026 Earnings Release, February 25, 2026)
Early 2026 validation deployments are confirmed at AWS, Google Cloud, Microsoft Azure, and Oracle Cloud, plus NVIDIA Cloud Partners CoreWeave, Lambda, Nebius, and Nscale.
Verified NVIDIA Vera Rubin NVL72 Rack Specifications
| Component | Verified Specification | Source |
|---|---|---|
| GPUs per rack | 72 Rubin GPUs | NVIDIA CES 2026 |
| CPUs per rack | 36 Vera CPUs — 88 Olympus Arm cores each, 176 threads (Spatial Multi-Threading) | NVIDIA CES 2026 |
| Inference compute | 3.6 ExaFLOPS (NVFP4) rack total | NVIDIA official product page |
| Training compute | 2.5 ExaFLOPS (NVFP4) rack total | NVIDIA official product page |
| HBM4 per GPU | 288 GB | NVIDIA CES 2026 |
| HBM4 bandwidth per GPU | 22 TB/s | NVIDIA CES 2026 |
| Total HBM4 per rack | 20.7 TB | VideoCardz, January 6, 2026 |
| Total HBM4 bandwidth per rack | 1.6 PB/s | VideoCardz, January 6, 2026 |
| CPU memory per rack | 54 TB LPDDR5X | NVIDIA CES 2026 |
| Scale-up interconnect | NVLink 6 — 3.6 TB/s per GPU all-to-all | NVIDIA CES 2026 |
| Scale-up bandwidth (rack total) | 260 TB/s | VideoCardz, January 6, 2026 |
| Scale-out networking | ConnectX-9 SuperNIC — 1.6 Tb/s per GPU | NVIDIA CES 2026 |
| Cooling | 100% liquid-cooled, cable-free modular tray | NVIDIA CES 2026 |
| GPU transistor count | 336 billion | NVIDIA CES 2026 |
| Process node | TSMC N3 | NVIDIA CES 2026 |
| Inference vs Blackwell | 5× (50 PFLOPS vs 10 PFLOPS per GPU, NVFP4) | NVIDIA stated |
| Cost per token vs Blackwell | 1/10th — MoE workloads | NVIDIA stated |
| Partner availability | H2 2026 | NVIDIA confirmed |
One figure not yet confirmed: NVIDIA has not officially disclosed per-GPU TDP for Vera Rubin. An estimate of ~2,300W has appeared in third-party analyst reports; this is not a confirmed NVIDIA figure.
NVLink 6 — What Changed from Blackwell
NVLink 6 doubles per-GPU bandwidth from 1.8 TB/s (NVLink 5 in Blackwell) to 3.6 TB/s by doubling the number of links per GPU from 18 to 36. (Source: NVIDIA technical documentation) The Spectrum-6 Ethernet Switch uses co-packaged optics (CPO) with 200G SerDes and 102 TB/s switching capacity — NVIDIA’s first production deployment of co-packaged optical switching. (Source: NVIDIA networking documentation)
Key Takeaway — Vera Rubin: The NVL72 is the highest-compute rack-scale AI system announced for 2026. Its 260 TB/s NVLink 6 all-to-all bandwidth is the defining architectural strength for MoE training workloads. The trade-off is full dependency on NVIDIA’s proprietary NVLink ecosystem. Availability: H2 2026.
What Is AMD Helios and How Does the MI455X Compare?
AMD CEO Lisa Su unveiled Helios and the MI400 series at CES 2026 on January 5–6, 2026. Physical chip samples were present on stage — Su held what AMD confirmed as the world’s first 2nm-class chips. This is a fully announced platform with published specifications.
Verified AMD Helios Rack Specifications
| Component | Verified Specification | Source |
|---|---|---|
| GPUs per rack | 72 Instinct MI455X | AMD CES 2026 |
| Total HBM4 memory | 31 TB | AMD CES 2026 |
| Aggregate memory bandwidth | 1.4 PB/s | AMD CES 2026 |
| Inference performance | 2.9 FP4 ExaFLOPS | AMD CES 2026 |
| Training performance | 1.4 FP8 ExaFLOPS | AMD CES 2026 |
| Scale-up bandwidth | 260 TB/s (UALink over Ethernet) | AMD CES 2026 |
| Scale-out bandwidth | 43 TB/s (Ultra Ethernet) | AMD CES 2026 |
| CPU | EPYC “Venice” (Zen 6, up to 256 cores per socket) | AMD CES 2026 |
| Rack weight | ~7,000 lbs | AMD CES 2026 |
| Cooling | Liquid-cooled | AMD CES 2026 |
| OCP design basis | ORW specification submitted by Meta | AMD technical blog |
| Availability | H2 2026 (confirmed) | AMD SVP Rick Norrod statement |
Verified AMD MI455X GPU Specifications
| Specification | Verified Value | Source |
|---|---|---|
| HBM4 per chip | 432 GB (12 × 36 GB HBM4 stacks) | AMD official; ServeTheHome CES 2026 |
| Memory bandwidth | 19.6 TB/s | AMD official |
| FP4 compute | 40 PFLOPs | AMD official |
| Transistor count | 320 billion | AMD official |
| Process node | TSMC N2 + N3 (mixed chiplet design) | AMD CES 2026 |
| Architecture | CDNA 5 | AMD official |
The Three MI400 SKUs — Confirmed Use Cases
MI455X is the flagship datacenter GPU for rack-scale AI inference and training, described above. MI440X targets enterprise on-premises deployments: 8-GPU server form factor, single EPYC Venice CPU, compatible with existing datacenter power and cooling — no rack-scale commitment required. MI430X is designed for sovereign AI and HPC workloads requiring full FP32 and FP64 precision; confirmed deployments include Oak Ridge National Laboratory’s Discovery system and France’s Alice Recoque exascale supercomputer.
The UALink Situation — What Is Actually Confirmed
At CES 2026, AMD confirmed the MI400 series would support UALink for scale-up connectivity — the first accelerators to do so. That statement remains accurate.
What has since been confirmed separately: native UALink switching silicon will not be available until 2027, per Astera Labs CEO Jitendra Mohan in an earnings call. Initial Helios shipments therefore use UALink over Ethernet (UALoE) — UALink tunneled over standard Ethernet switching hardware, using ASICs such as Broadcom’s Tomahawk 6.
UALoE and native UALink are different performance profiles. AMD has not published bandwidth or latency figures differentiating the two in Helios configurations. The UALink standard remains a credible open-standard alternative to NVLink long-term — the consortium includes AMD, Google, Intel, Meta, and Microsoft. The 2027 native UALink timeline is a supply chain dependency, not a strategic change of direction.
Confirmed AMD Customers (as of March 2026)
OpenAI: 6 GW infrastructure deal announced October 2025; first 1 GW MI450 deployment beginning H2 2026. Oracle: confirmed first in line for MI450 series, 50,000 GPU sockets. HPE: adopted Helios rack architecture for its 2026 AI systems. Meta: co-developed the Open Rack Wide (ORW) specification on which Helios is based.
A Precise Note on AMD’s MI500 “1,000×” Claim
AMD previewed the MI500 Series for 2027 with a stated “1,000× performance increase over MI300X.” AMD subsequently clarified this comparison is between an 8-GPU MI300X node and an MI500 rack system with an unspecified number of GPUs — not a chip-to-chip comparison. MI500 confirmed details: CDNA 6 architecture, TSMC advanced 2nm process, HBM4e memory. Specific specifications have not been published.
Key Takeaway — AMD Helios: Helios delivers 50% more HBM4 memory per chip (432 GB) and 50% more total rack memory (31 TB) than Vera Rubin NVL72. The trade-off: initial interconnect is UALoE, not native UALink; native UALink silicon ships in 2027. For memory-constrained inference workloads and organizations prioritizing open standards, AMD is a technically credible and commercially committed platform.
What Is Google Ironwood TPU and Why Does It Matter?
Google announced Ironwood at Google Cloud Next in April 2025 and confirmed general availability in November 2025. Ironwood is currently available to Google Cloud customers — it is not a future platform.
Verified Google Ironwood TPU Specifications
| Specification | Verified Value | Source |
|---|---|---|
| Compute performance | 4,614 FP8 TFLOPS per chip | Google Cloud official documentation |
| Memory per chip | 192 GB HBM3E | Google Cloud documentation |
| Memory bandwidth per chip | 7.37 TB/s | Google Cloud documentation |
| Inter-Chip Interconnect (ICI) | 9.6 Tb/s bidirectional per chip (4 links × 1.2 TB/s) | Google Cloud documentation |
| Chips per Superpod | 9,216 | Google Cloud documentation |
| Superpod compute | 42.5 FP8 ExaFLOPS | Google Cloud documentation |
| Superpod memory | 1.77 PB HBM3E | Google Cloud documentation |
| Chip architecture | Two chiplets: 1 TensorCore + 2 SparseCores + 96 GB HBM per chiplet | Google Cloud documentation |
| Interconnect type | Optical Inter-Chip Interconnect | Google Cloud documentation |
| Availability | General Availability — Google Cloud, November 2025 | Google Cloud blog |
| Access method | Google Kubernetes Engine (GKE) | Google Cloud documentation |
The Pod Scale Comparison — Context Required
The 42.5 ExaFLOPS Superpod figure uses 9,216 chips — 128× the number of GPUs in a single NVIDIA NVL72 rack. At the individual chip level, Ironwood (4,614 FP8 TFLOPS) and NVIDIA B200 (4,500 FP8 TFLOPS) are broadly comparable. Ironwood’s structural advantage is its ability to connect 9,216 chips via optical ICI into a single logical unit with terabit-class per-chip interconnect bandwidth. No GPU rack configuration achieves equivalent scale with comparable interconnect efficiency in a commercially available product today.
Software Access: JAX and TorchXLA
Ironwood pods require JAX or PyTorch/XLA (TorchXLA) as the programming interface. Standard PyTorch workloads require TorchXLA bridging — this is a real but manageable porting overhead for teams without prior TPU experience. Google has been actively expanding TorchXLA support to reduce adoption friction. (Source: PyTorch/XLA documentation)
Confirmed Ironwood Customers
Anthropic: confirmed commitment to deploy up to one million Ironwood TPUs for Claude model training and serving. Lightricks: confirmed Ironwood use for LTX-2 multimodal system. Google: confirmed internal use for production Gemini deployments.
Key Takeaway — Google Ironwood: The only platform in this comparison that is currently available. Per-chip performance is comparable to NVIDIA B200; Superpod-scale optical interconnect is architecturally distinct. The meaningful trade-off is the software porting requirement (JAX or TorchXLA) and Google Cloud exclusivity. For organizations already on Google Cloud or willing to adopt TorchXLA, Ironwood’s inference economics and availability are compelling.
Architecture Comparison: Verified Numbers Side by Side
The following tables contain only confirmed, source-attributed figures.
Per-Chip Compute, Memory, and Bandwidth
| Platform | Compute | Memory | Bandwidth | HBM Gen | Availability |
|---|---|---|---|---|---|
| NVIDIA Rubin GPU | 50 PFLOPS NVFP4 inference (no FP8 figure published by NVIDIA) | 288 GB | 22 TB/s | HBM4 | H2 2026 |
| AMD MI455X | 40 PFLOPS FP4 / 20 PFLOPS FP8 (derived) | 432 GB | 19.6 TB/s | HBM4 | H2 2026 |
| Google Ironwood TPU | 4,614 FP8 TFLOPS | 192 GB | 7.37 TB/s | HBM3E | Available now |
| NVIDIA B200 (current-gen) | 4,500 FP8 TFLOPS | 192 GB | 8.0 TB/s | HBM3E | Available now |
Note: NVIDIA quotes Rubin performance in NVFP4, a mixed-precision format. This is not directly comparable to FP8 figures from AMD or Google. NVIDIA has not published an FP8 figure for Vera Rubin.
Rack / Pod Scale Comparison
| Platform | Chips | Compute | Total Memory | Interconnect BW | Availability |
|---|---|---|---|---|---|
| NVIDIA Vera Rubin NVL72 | 72 GPUs | 3.6 ExaFLOPS (NVFP4) | 20.7 TB HBM4 + 54 TB LPDDR5X | 260 TB/s NVLink 6 | H2 2026 |
| AMD Helios | 72 MI455X | 2.9 ExaFLOPS (FP4) | 31 TB HBM4 | 260 TB/s UALoE | H2 2026 |
| Google Ironwood Superpod | 9,216 chips | 42.5 ExaFLOPS (FP8) | 1.77 PB HBM3E | 9.6 Tb/s ICI per chip | Available now |
Interconnect Architecture
| Platform | Interconnect | Architecture | Open or Proprietary | Key Characteristic |
|---|---|---|---|---|
| NVIDIA Vera Rubin NVL72 | NVLink 6 | Electrical, all-to-all | Proprietary | 3.6 TB/s per GPU; 260 TB/s rack total |
| AMD Helios (initial H2 2026) | UALink over Ethernet (UALoE) | Ethernet-tunneled | Open (Ethernet) | Native UALink silicon ships 2027 |
| Google Ironwood | ICI (Inter-Chip Interconnect) | Optical | Proprietary to Google | 9.6 Tb/s per chip; 9,216-chip Pods |
What GTC 2026 Will Cover — Confirmed vs. Not Confirmed
| Topic | Status | Source |
|---|---|---|
| Jensen Huang keynote, March 16, 11 a.m. PT, SAP Center | ✅ Confirmed | NVIDIA press release, March 3, 2026 |
| 30,000+ attendees, 190+ countries, 1,000+ sessions | ✅ Confirmed | NVIDIA press release, March 3, 2026 |
| Investor Q&A, March 17, 9 a.m. PT | ✅ Confirmed | NVIDIA press release, March 3, 2026 |
| Frontier open models discussion (A16Z, AI2, Black Forest Labs, Cursor, Reflection AI, Thinking Machines) | ✅ Confirmed | NVIDIA press release, March 3, 2026 |
| A chip reveal described as a “surprise” | ✅ Confirmed — reveal is confirmed, content is not | Jensen Huang via Korea Economic Daily / VideoCardz |
| Which chip or product will be revealed | ❌ Not confirmed | No official NVIDIA statement as of March 4, 2026 |
| Feynman architecture announcement | ❌ Not confirmed | Industry speculation only |
| NVIDIA N1X laptop CPU | ❌ Not confirmed | Rumored; no NVIDIA statement |
| Vera Rubin production timeline update | Not announced — Vera Rubin is already confirmed in production | Consistent with prior NVIDIA communications |
| Rubin Ultra NVL576 detail update | Possible — NVL576 was disclosed at CES 2026 | CES 2026 roadmap slide |
The Software Layer: Why Specs Are Not the Whole Story
Hardware specifications determine the performance ceiling. Software ecosystems determine how much of that ceiling is reachable in practice.
NVIDIA CUDA remains the default compilation target for PyTorch, TensorFlow, and the dominant majority of production ML frameworks. The advantage is not in framework-level support — that has largely converged — but in two decades of accumulated custom kernel optimization. Attention mechanisms, quantization routines, and sparse operations have been fine-tuned by thousands of researchers specifically for CUDA. These optimizations do not automatically transfer to other platforms.
AMD ROCm has reached practical parity with CUDA for standard PyTorch and TensorFlow training workloads on most published benchmarks. AMD’s HIPIFY tool automates CUDA-to-HIP porting for a large fraction of standard operator code. Remaining gaps concentrate in sparse operator libraries and custom CUDA kernels without ROCm equivalents. For new projects with no legacy CUDA code, ROCm is a manageable engineering overhead. For organizations with large production CUDA codebases, migration is a multi-year engineering program.
Google JAX and TorchXLA are the programming interfaces for Ironwood TPUs. JAX offers strong performance for training workloads expressed as pure functional transformations. TorchXLA — also referred to as PyTorch/XLA — allows PyTorch models to run on TPUs with lower porting overhead. Google has been actively expanding TorchXLA compatibility. (Source: pytorch.org/xla)
Verified open-source tools that work across platforms:
- vLLM — production LLM serving with NVIDIA and AMD backend support via a unified API
- AMD ROCm — AMD’s official software platform
- PyTorch/XLA — Google TPU support for PyTorch
- NVIDIA CUDA Toolkit — NVIDIA’s official developer platform
- Ray — distributed AI compute orchestration, multi-backend
Workload Matching: Which Hardware for Which Job
The following recommendations follow directly from the verified specifications. They are architectural logic, not vendor marketing.
| Workload | Recommended Platform | Reason |
|---|---|---|
| Frontier model training — MoE, >100B parameters | NVIDIA Vera Rubin NVL72 | 260 TB/s NVLink 6 all-to-all bandwidth is required for MoE all-to-all expert communication at this scale; CUDA ecosystem for distributed training frameworks |
| Large-scale inference — throughput-optimized | Google Ironwood Superpod | 42.5 ExaFLOPS at pod scale; available now; optimal for Google Cloud workloads using JAX or TorchXLA |
| Enterprise on-premise — training + fine-tuning | AMD Helios MI440X | Standard rack form factor; EPYC Venice + 8× GPU; fits existing datacenter power and cooling; no rack-scale commitment |
| Sovereign AI / HPC — full floating point precision | AMD MI430X | Full FP32 and FP64 confirmed; deployed at Oak Ridge (Discovery) and France (Alice Recoque) |
| High-memory inference — large models per chip | AMD MI455X | 432 GB HBM4 per chip — the highest confirmed per-chip capacity of any announced 2026 accelerator |
| Immediate deployment — CUDA codebase | NVIDIA Blackwell B200 | Available now; 192 GB HBM3E; mature CUDA ecosystem; no H2 2026 wait |
On the Blackwell vs. Vera Rubin decision: For deployments required in Q2–Q3 2026, Blackwell B200 is available today. Vera Rubin NVL72 partner availability is confirmed for H2 2026. Vera Rubin delivers 5× inference performance improvement over Blackwell. Whether this improvement justifies a 6–9 month deployment delay depends entirely on workload urgency. Neither option is wrong; the decision is a function of timeline.
NVIDIA’s Confirmed Roadmap — What Comes After Vera Rubin
NVIDIA disclosed this roadmap publicly at CES 2026. The following entries are confirmed disclosures, not speculation:
| Platform | Target | Confirmed Detail |
|---|---|---|
| Blackwell | 2024/2025 | Shipping now; B200 GPU — 192 GB HBM3E, 4,500 FP8 TFLOPS |
| Vera Rubin | 2026 | NVL72 confirmed; partner shipments H2 2026 |
| Rubin Ultra | 2027 | NVL576 disclosed at CES 2026 — 576 GPUs, 15 ExaFLOPS (NVFP4) |
| Feynman | 2028 | Name confirmed on NVIDIA roadmap slide; no specifications published |
Any claims about Feynman’s process node, transistor count, or performance are speculative at this time. NVIDIA has published no specifications for Feynman beyond its position on the roadmap timeline.
Frequently Asked Questions
What are the confirmed specifications of the NVIDIA Vera Rubin NVL72?
The NVIDIA Vera Rubin NVL72 rack contains 72 Rubin GPUs and 36 Vera CPUs. It delivers 3.6 ExaFLOPS (NVFP4) inference and 2.5 ExaFLOPS (NVFP4) training at rack scale. Each GPU has 288 GB HBM4 at 22 TB/s bandwidth; total rack HBM4 is 20.7 TB at 1.6 PB/s. Scale-up interconnect is NVLink 6 at 3.6 TB/s per GPU, 260 TB/s rack total. Cooling is 100% liquid. Partner availability is confirmed for H2 2026. (Source: NVIDIA official product page and CES 2026 announcement)
How does AMD Helios compare to NVIDIA Vera Rubin NVL72?
AMD Helios has 31 TB HBM4 total versus NVIDIA Vera Rubin NVL72’s 20.7 TB HBM4 — a confirmed 50% rack-level memory advantage for AMD. Per chip: AMD MI455X has 432 GB HBM4 versus NVIDIA Rubin’s 288 GB — a 50% per-chip memory advantage for AMD. Compute: AMD delivers 2.9 FP4 ExaFLOPS versus NVIDIA’s 3.6 NVFP4 ExaFLOPS, but these use different precision formats and are not directly comparable. Both target H2 2026. Initial AMD interconnect is UALoE; NVIDIA uses proprietary NVLink 6. (Source: AMD CES 2026; NVIDIA CES 2026)
What is the Google Ironwood TPU and what does it perform at?
Google Ironwood (TPU v7) delivers 4,614 FP8 TFLOPS per chip with 192 GB HBM3E at 7.37 TB/s bandwidth. A full 9,216-chip Superpod delivers 42.5 FP8 ExaFLOPS with 1.77 PB of aggregate HBM3E memory. Google Ironwood reached general availability on Google Cloud in November 2025. Programming requires JAX or PyTorch/XLA (TorchXLA). (Source: Google Cloud official documentation)
Should I deploy NVIDIA Blackwell B200 now or wait for Vera Rubin?
For deployments required in Q2–Q3 2026, Blackwell B200 is available today with a mature CUDA ecosystem and 192 GB HBM3E per GPU. Vera Rubin NVL72 delivers 5× inference performance over Blackwell and costs 1/10th per token on MoE workloads — but partner availability is confirmed for H2 2026. The decision is a function of your deployment timeline, not hardware quality. Neither platform is the wrong choice for its respective use case.
What exactly did NVIDIA confirm about chip announcements at GTC 2026?
NVIDIA confirmed GTC 2026 runs March 16–19 in San Jose with Jensen Huang keynote on March 16 at 11 a.m. PT. Jensen Huang separately stated NVIDIA will reveal “a chip that will surprise the world.” NVIDIA has not identified which product category this refers to. Any article claiming to specify the content of this announcement before March 16 is speculating. (Source: NVIDIA press release March 3, 2026; VideoCardz citing Korea Economic Daily)
Is the AMD MI455X delayed?
AMD has explicitly denied the delay report from SemiAnalysis. AMD SVP Rick Norrod confirmed Helios systems are “on target for 2H 2026.” Separately, native UALink switching silicon from ecosystem partners (Astera Labs and others) will not be available until 2027 — confirmed by Astera Labs CEO Jitendra Mohan. Initial Helios shipments will use UALink over Ethernet rather than native UALink switching. AMD has not characterized this as a delay. (Source: AMD statement; Astera Labs earnings call)
The Code: AI Hardware 2026 Interactive Course
AI Hardware Showdown 2026 – Interactive Course + Quiz
Sources — Primary and Verified Secondary
All specifications used in this article are sourced from the following:
- NVIDIA GTC 2026 Official Press Release — NVIDIA Newsroom, March 3, 2026
- NVIDIA Vera Rubin NVL72 Official Product Page — nvidia.com
- NVIDIA Vera Rubin CES 2026 Specs — Tom’s Hardware, January 5, 2026
- NVIDIA Vera Rubin NVL72 Architecture Detail — VideoCardz, January 6, 2026
- NVIDIA “Surprise” Chip Confirmed for GTC — VideoCardz
- NVIDIA Q4 FY2026 Earnings Release — February 25, 2026
- AMD MI400 Series + Helios — Tom’s Hardware CES 2026, January 6, 2026
- AMD Helios Technical Blog — amd.com
- AMD MI455X Physical Preview — ServeTheHome, CES 2026
- AMD Denies MI455X Delay — Tom’s Hardware, February 2026
- Google Ironwood TPU Official Blog — Google Cloud, April 2025
- Google Cloud TPU v7 (Ironwood) Official Documentation
- Google Ironwood GA — Tom’s Hardware, November 2025
- AMD ROCm Official Documentation
- PyTorch/XLA (TorchXLA)
- vLLM Multi-Backend Inference — GitHub
Related Articles on NeuralCoreTech:
- 🔗 NVIDIA Vera Rubin vs Blackwell: The AI Hardware Shift That Will Redefine 2026
- 🔗 Core AI Infrastructure 2026: Hardware, Models & AI Infrastructure Explained
- 🔗 Best AI Tools 2026: ChatGPT Just Hit 900M — What Happens Next?
© 2026 NeuralCoreTech. All specifications sourced from official manufacturer announcements, verified primary technical documentation, and named secondary sources. Claims described as “confirmed” reflect official manufacturer or primary source statements. Items described as estimates, projections, or analyst views are explicitly identified as such throughout the article. This article does not constitute investment or procurement advice.
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