AI Compute Cards Wiki Editorial

August 23-25, 2026, the 38th Hot Chips (HC38) was held at Stanford's Memorial Auditorium. As the bellwether of global high-performance chip architecture, this conference landed exactly at the most intense moment of the AI compute arms race — the official agenda had 48 entries, including 7 AI accelerators, 6 memory tutorials, 6 CPUs, and 4 each of GPUs and networking. Putting the vendor talks together, one consensus emerged: the unit of AI compute competition has shifted from "single chip" to "whole rack / entire system."

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1. Overview: Three Days of Agenda, Almost a Preview of the 2027 AI Rack Market​

Monday (8/24) afternoon's GPU session was the focus, with four talks nearly colliding as the 2027 AI rack market:

• NVIDIA Rubin GPU ("Driving the Era of Agentic AI"): First chiplet-architecture GPU, 288GB HBM4, ~50 PFLOPS FP4, paired with 88-core Arm-architecture Vera CPU into NVL72 / NVL144 racks, mass production in H2 2026.
• AMD Instinct MI400 (two talks: architecture + system architecture): Told the "rack-scale" story thoroughly.
• Intel Crescent Island: A 350W air-cooled card designed for Agentic AI inference.

Tuesday (8/25) afternoon's AI session was almost a parade of "hyperscalers de-NVIDIA-izing": Google's 8th-gen TPU, OpenAI's first custom chip, Microsoft Maia 200, Meta MTIA, and Cerebras wafer-scale rack all appeared together.

Every vendor on stage used the term "Agentic AI" within the first two PPT slides — not a coincidence, but the collective shift in 2026 AI workload design goals.

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2. NVIDIA Rubin: One Rack Is a Supercomputer​

What NVIDIA featured at Hot Chips was not a single GPU but the Vera Rubin NVL72 whole cabinet — 72 Rubin GPUs + 36 Vera CPUs, 18 compute trays + 9 NVLink switch trays, about 1.3 million components, nearly 1,300 chips, weighing about 4,000 pounds (~1.8 tons).

The single Rubin GPU specs are equally stunning:

Metric	Rubin GPU	vs Blackwell
Transistors	336 billion (TSMC 3nm dual-die)	208 billion (+61.5%)
Memory	288GB HBM4	—
Bandwidth	22 TB/s	2.8× Blackwell
NVFP4 inference	50 PFLOPS	5× GB200
Training compute	35 PFLOPS	3.5×

The most disruptive design is in the compute tray: no cables, no hoses, no fans, all interconnected via the PCB backplane. NVIDIA says assembly time dropped from nearly 2 hours to 5 minutes (20× faster) while improving maintainability.

This time NVIDIA is selling not FLOPS but tokens per megawatt. Citing a SemiAnalysis benchmark based on DeepSeek-v4-PRO (140K+ context, AgentX workload), it claims: versus GB300 NVL72, Vera Rubin NVL72 delivers 10× to up to 30× tokens/MW as interaction intensity rises. A single cabinet provides 3.6 EFLOPS inference compute, whole-cabinet power 190-230kW; long-term capacity target is 1,000 NVL72 cabinets per day.

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3. AMD MI455X + Helios: Bigger Memory and Open Interconnect​

AMD's answer is the MI455X + Helios rack going head-to-head with NVIDIA. MI455X uses CDNA 5 architecture, 8 N2-process accelerator dies + N3P-process interconnect die, 256 workgroup processors, 192MB global L2.

Metric	MI455X	vs Rubin
Memory	432GB HBM4 (12-layer stack)	50% higher than Rubin's 288GB
Bandwidth	23.3 TB/s	Slightly ahead
MXFP4 compute	40.26 PFLOPS	—
System (Helios 72 cards)	2.9 ExaFLOPS FP4 inference	—
Price	~$5.25M per cabinet	—

At the system level, AMD bets on the UALoE (Ultra Accelerator Link over Ethernet) open standard: each GPU provides 3.6 TB/s bidirectional interconnect bandwidth; two 512-port 200G UALoE switch chips in the switch tray total 10.8 TB/s — opening the interconnect protocol to the whole industry while targeting NVLink.

Production cadence: AMD plans to deliver engineering samples and small-batch systems in H2 2026, with large-scale ramp in Q2 2027. Earlier rumors of Helios delay due to cooling issues were not confirmed by AMD.

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4. Intel Crescent Island: The Air-Cooled, Large-Memory "Cost-Effective Oddball"​

Intel offers a completely different path: Crescent Island — a 350W, air-cooled, standard-PCIe-slot inference GPU designed for Agentic AI, with the key metric being tokens per watt.

Metric	Crescent Island	Note
Architecture	Xe3P, 32 Xe cores, 32MB unified L2	Disclosed at Hot Chips
Memory	Intel branded card 160GB / ODM up to 480GB LPDDR5X	More than Rubin's 288GB HBM4
Form factor	350W air-cooled PCIe	Plugs into standard racks, no liquid-cooling retrofit
RAS	ECC, dynamic page offline, hard-package repair, PCIe advanced error reporting	Addresses "silent data corruption"

Intel's logic is clear: inference scenarios need far more memory capacity than bandwidth; using low-cost LPDDR5X for capacity and air cooling to skip liquid-cooling infrastructure drives down per-token cost. Combined with Diamond Rapids Xeon (256 performance cores, 1.28GB cache, 128 PCIe Gen6 lanes), Intel tries to surround from edge to datacenter with "CPU + inference GPU + open software stack."

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5. Custom ASIC Parade: Google, OpenAI, Microsoft, Meta Together​

Tuesday afternoon's AI session was the most historic of the conference — a parade of "hyperscalers de-NVIDIA-izing":

Chip	Vendor / Partner	Positioning	Key Specs / Progress
TPU 8t (Sunfish)	Google × Broadcom	Training	9,600 cards per pod, 121 FP4 ExaFLOPS, 2PB shared HBM
TPU 8i (Zebrafish)	Google × MediaTek	Inference	288GB HBM, 384MB on-chip SRAM (3× prev gen), ICI 19.2 Tb/s
Jalapeño	OpenAI × Broadcom	Inference	9-month end-to-end design, target ~50% token cost cut, commercial end of 2026
Maia 200	Microsoft (TSMC 3nm)	Inference	140B+ transistors, 10+ PFLOPS FP4, 216GB HBM3E, serving GPT-5.2 at Des Moines datacenter
MTIA 300-500	Meta (RISC-V) × Broadcom	Training + inference	Up to 25× compute gain, one model every 6 months before 2027

Google split TPU into training (8t) and inference (8i) dedicated architectures for the first time — its biggest architectural shift in a decade. Norm Jouppi personally took the stage to present TPU v8.

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6. Two Hidden Threads — Memory and Networking: HBM4 Year 1 + AI Factory OS​

Beyond GPUs/ASICs, two hidden threads mattered equally:

• Memory: Samsung's HBM Base Die (logic-process base die) and SK hynix's advanced packaging appeared together; the HBM4-era "base-die foundry" industry shift begins; HBF (high-bandwidth flash), LPDDR5X-PIM, 3D DRAM, and CXL compute-storage showcased "compute-in-memory" moving from papers to products.
• Networking: NVIDIA BlueField-4 (DPU) and Spectrum-X Multiplane architecture (presented by Gilad Shainer) — networking is becoming the decisive architecture for gigascale AI, scaling from hundreds of thousands to a million cards; Broadcom Thor Ultra Ethernet NIC keeps pressing; Mojo Vision showed chip-level optical I/O.

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7. Three Routes, One Consensus​

At the same conference, three vendors offered three distinctly different AI compute delivery philosophies:

1. NVIDIA: Full-stack closed integration — GPU, CPU, DPU, and switch chips all self-designed, pushing system performance to the extreme via ultimate software-hardware co-design, at the cost of deep customer lock-in.
2. AMD: Open-standard catch-up — Uses larger HBM4 capacity + UALoE open interconnect for a "cost-effective + open" play, tearing open the inference gap with Meta and OpenAI's 12GW-class orders.
3. Intel: Air-cooled cost-effectiveness — Abandons liquid cooling and HBM, uses LPDDR5X large memory + standard PCIe, betting that "most inference doesn't need a 200kW rack."

But all three agree: the unit of competition is no longer the chip, but the co-designed system (rack / system). For buyers, 2027 compute planning should compare not "single-card PFLOPS" but "tokens per megawatt, latency, availability, and full-lifecycle cost."

Related Links​

• NVIDIA Rubin spec page
• AMD MI455X spec page
• Intel Crescent Island spec page
• Google TPU 8t spec page
• Google TPU 8i spec page

References​

• Hot Chips 2026 Three Giants Together: Rubin 336B Transistors, MI455X 432GB, Crescent Island 480GB - IC Chip Industry Watch
• Hot Chips Day 2: Rubin, MI400 and Diamond Rapids Go Head-to-Head - AI2Work
• NVIDIA Vera Rubin NVL72 Rack at Hot Chips 2026 - ServeTheHome
• Hot Chips 2026: 4 Hyperscalers Put Custom AI Silicon on One Afternoon - Sean Kim

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This article is compiled from Hot Chips 2026 (Aug 23-25) official presentations and on-site reports from ServeTheHome, SemiAnalysis, TechPowerUp, etc. Performance data are vendor-disclosed figures; actual performance subject to mass-produced products.

The Tuesday-afternoon AI session at Hot Chips 2026 this August was the most historically significant of the conference — not because any single chip was so powerful, but because almost everything on stage was a "hyperscaler de-NVIDIA-ization" custom ASIC: Google's 8th-gen TPU, OpenAI's first self-designed chip, Microsoft Maia, Meta MTIA, and Cerebras wafer-scale racks, all on one stage. When the world's largest AI compute buyers start treating GPUs as "one of the options," the power structure of AI hardware is loosening.

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1. OpenAI Jalapeno: Building a Chip in 9 Months​

On June 24, 2026, OpenAI, together with Broadcom, unveiled its first self-designed inference ASIC, Jalapeno — the fifth member of the "custom inference chip club."

Dimension	Jalapeno
Partner	Broadcom + TSMC manufacturing
Positioning	Inference-specific ASIC
Design cycle	9 months end-to-end (Greg Brockman says aided by OpenAI's own models)
Cost target	~50% lower token cost vs general-purpose GPU stack
Commercial timing	First deployments by end-2026; long-term goal 10GW of self-designed chips
Deal scale	Up to $10B strategic partnership with Broadcom (accelerators + networking by 2029)

The talk title "You Can Just Build Things … Chips" is itself a signal: the largest AI compute buyer no longer defaults to GPU as the only path.

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2. Google TPU v8: The Biggest Architectural Pivot in a Decade — Train/Infer Split​

Google has the longest custom-chip history (2016 to now), and its 8th-gen TPU for the first time splits the product line in two:

Model	Codename	Partner	Positioning	Key Specs
TPU 8t	Sunfish	Broadcom	Training	9,600 cards per pod, 121 FP4 ExaFLOPS, 2PB shared HBM, 2× ICI bandwidth
TPU 8i	Zebrafish	MediaTek	Inference	288GB HBM, 384MB on-chip SRAM (3× prior gen), 19.2 Tb/s ICI

On capacity, Morgan Stanley estimates based on supply-chain interviews that Google TPU production in 2026 may exceed 3 million units (a brokerage estimate, not an official target). Google is also the only vendor to achieve large-scale custom-chip deployment and sell compute externally (Gemini runs on TPUs).

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3. Meta MTIA: From Recommendation Systems to a GenAI Dual Mission​

Meta's custom journey has the clearest starting point — MTIA was originally built for recommendation ranking hardware and is being pulled toward a dual mission by generative AI.

• MTIA 300 is deployed; 400 / 450 / 500 are planned at roughly one new model every 6 months through 2027;
• Based on RISC-V, Meta claims up to 25× compute gain;
• Node evolves with industry cadence: 100 (7nm) → 200 (5nm) → 300 series (3nm + CoWoS);
• In partnership with Broadcom; another chip codenamed Iris reportedly passed testing in July 2026;
• Meta plans to start volume production of one of them in September 2026, doubling its overall compute.

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4. Microsoft Maia 200/300: Most Advanced Deployment​

Microsoft's Maia 200, released January 26, 2026, is the most advanced in deployment among the four:

Dimension	Maia 200
Process	TSMC 3nm, 140B+ transistors
Compute	10+ PFLOPS FP4 / 5 PFLOPS FP8
Memory	216GB HBM3E, 7 TB/s
Power	750W
Deployment	Already running in Des Moines data center, serving OpenAI GPT-5.2 and Microsoft 365 Copilot

Microsoft claims roughly 3× the performance of Amazon's Trainium on specific benchmarks. The short-term strategy is a dual track of "self-designed Maia + purchased NVIDIA" in parallel — self-designed chips need time from design to mass production, and NVIDIA's mature ecosystem cannot be replaced in the short term.

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5. Amazon Trainium 3 and Anthropic's In-House Team​

• Amazon: The Trainium series is already commercial, with 1.4 million units cumulatively deployed (officially disclosed) — a multi-billion-dollar business; its strength is the AWS customer base, letting enterprises choose between NVIDIA GPUs and self-designed chips. Trainium 3 continues this path.
• Anthropic: In August 2026 announced the formation of an in-house chip team, with no tape-out or mass-production timeline yet; initially positioned as a complement (not a replacement) to existing partnerships with NVIDIA/AMD/AWS/Google Cloud, aiming to tailor-build for the Claude architecture and shed reliance on a single GPU.

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6. NVIDIA's Answer: Not a Faster GPU, But Full-Stack​

It's easy to simplify the narrative to "four companies build chips, NVIDIA defends GPU." But NVIDIA took 6 slots at Hot Chips: a RISC-V tutorial, the Vera CPU, the Rubin GPU, the BlueField-4 DPU, the Spectrum-X multi-plane network, and an LPU accelerator.

A hyperscaler ASIC replaces only one of those five pillars. If the CPU, NIC, switching fabric, and software all come from the same vendor, what you save by swapping out the accelerator is far less than the accelerator line item on the bill suggests. Rubin's play is a full-stack AI factory platform spanning seven chips and five racks — the competitive answer is "full-stack positioning," not "a faster single chip."

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7. Trend Judgment: Inference De-GPU-izes, Training Still GPU-Led​

• Inference side: The CUDA moat visibly shallows. Inference is parallelizable and replaceable at the endpoint; custom ASICs trade away the generality tax (implementing only the operations LLMs actually execute) for lower cost/token. Groq LPU, Cerebras, and various TPU/ASIC players all compete on the same metric.
• Training side: Foundation models are still trained on GPUs, with no serious challenger in the short term. NVIDIA's three training moats (fastest silicon + NVLink + CUDA) remain firm.
• Conclusion: Custom chips are not "replacing NVIDIA," but giving buyers a credible external negotiation option in the largest and fastest-growing battlefield — inference. That alone is enough to reshape the economics of AI infrastructure.

Related Links​

• Inference Accelerator Market 2026
• Hot Chips 2026 Full Recap
• Google TPU 8t Spec Page
• Google TPU 8i Spec Page

References​

• Hot Chips 2026: 4 Hyperscalers Put Custom AI Silicon on One Afternoon - Sean Kim
• Custom AI inference silicon: why OpenAI built Jalapeno - nexi.fund
• Anthropic Builds Custom AI Chips for Claude - Studio Global AI
• 所有大厂都在做同一件事：自己造芯片，少买英伟达的 - Toutiao

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This article is compiled from August 2026 Hot Chips on-site reports, corporate announcements, and industry analysis. Some capacity and performance figures are brokerage estimates or vendor-disclosed figures; actual results are subject to mass-produced products.

If 2025 was the year of HBM3E capacity ramp-up, then 2026 is year one of HBM4 mass production. With NVIDIA Vera Rubin and AMD MI400 — two generations of flagship — both betting on HBM4, this "memory on the AI chip" has for the first time become a strategic commodity that dictates the delivery pace of entire racks. The yield and certification data disclosed densely in August is rewriting the global HBM supply map.

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1. Golden Yield Breakthrough: Samsung Jumps from Under 60% to 80% in Six Months​

Per South Korea's Seoul Economic Daily on August 9, Samsung Electronics' HBM4 yield officially crossed the 80% "golden yield" threshold in early August — more than four months ahead of its original year-end target.

Timeline	Samsung HBM4 Yield	Notes
Feb 2026 (mass production start)	Under 60%	Line ramp-up period
Early Aug 2026	~80%	Crosses the mass-production / stable-profit watershed

The semiconductor industry has long held that "80% yield is the golden yield" — it is both a yardstick of foundry competitiveness and the financial break-even point for large-scale commercial supply. The key to this leap was Samsung's breakthrough in Thermal Compression Non-Conductive Film (TC-NCF) bonding, plus the stable base of its underlying 1c DRAM yield, already above 80%. In the same period, Samsung's HBM4E reliability test yield also broke 70%.

Industry assessments suggest SK Hynix's HBM4 yield has likewise entered the 80% range. The gap between the two giants in production quality is being rapidly erased.

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2. Supply Map: SK Hynix Holds 60–70% of Rubin Allocation​

At a Seoul event on June 5, Jensen Huang publicly confirmed: Samsung, SK Hynix, and Micron have all passed HBM4 certification for Vera Rubin — the first time three memory makers have simultaneously received public certification for the same platform.

But certification is just the "entry ticket" — allocation share is where the real voice lies:

Vendor	2026 Rubin HBM4 Allocation (est.)	Notes
SK Hynix	60%–70%	Based on HBM3/3E-era customer relationships and MR-MUF packaging
Samsung	25%–30%	Rapid share gains after yield leap
Micron	Remainder	Limited HBM4 exposure, relatively stable share

Counterpoint Research forecasts the 2026 HBM4 market as SK Hynix 54% / Samsung 28% / Micron 18%. Samsung has set staged catch-up targets: Q3 HBM4 revenue up 3× QoQ, HBM4 exceeding 60% of total HBM revenue in H2, and year-end overall HBM market share approaching 38%.

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3. The Real Bottleneck: From Wafers to "Back-End Stacking"​

As front-end yield stabilizes, the rhythm of the AI accelerator supply chain no longer depends on "how many wafers can be made," but on the speed of back-end stacking, bonding, testing, and shipment.

• Industry analysts rank HBM stacking as the second-most severe bottleneck in the AI chip supply chain, second only to TSMC's CoWoS advanced packaging capacity.
• HBM accounts for roughly 25% of 2026 DRAM wafer output; each HBM wafer consumes about 3–4× the resources of a standard DRAM wafer (extra TSV and stacking steps), so every wafer redirected pulls 3–4 units of commodity memory off the spot market.
• Samsung is considering relocating part of its legacy memory back-end lines (Cheonan, Onyang) to Vietnam to free up HBM back-end capacity — a side confirmation that back-end throughput is now the tightest link in the chain.

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4. HBM4 Spec Snapshot: Generational Leap in Bandwidth and Efficiency​

Spec	HBM4 (12-Hi / 16-Hi)	HBM4E
Per-stack capacity	36 GB / 48 GB	—
Pin rate	11.7–13.0 Gbps	16 Gbps
Per-stack bandwidth	up to 3.3 TB/s	up to 3.6 TB/s
Bus width	2048-bit	—
Energy efficiency	+40% vs HBM3E	—
Thermal resistance / cooling	+10% improvement / +30%	—

Samsung HBM4 entered mass production in Feb 2026; its 11.7 Gbps pin rate already exceeds the 8 Gbps industry baseline required for Vera Rubin compatibility; HBM4E samples were first shipped to major customers on May 29.

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5. Pricing Power Extends Into 2027: Supply Remains Tight Balance​

TrendForce judges that HBM suppliers' pricing power will run through 2027, because supply remains constrained:

• 2027 HBM bit shipments are expected to grow 50%–60% YoY, but will still lag demand growth, keeping the market tight;
• The industry already anticipates significant price increases;
• For NVIDIA and AMD, a stronger Samsung means more supply options and more comfortable lead times — in a market where memory is the tightest link in AI servers, the mere existence of second and third suppliers is itself a buffer.

For entire racks, HBM cost is already the biggest driver: the Rubin Ultra rack carries an estimated price tag as high as $21 million, with HBM making up a substantial portion.

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6. Lessons for China: HBM Export Controls Accelerate Domestic Iteration​

HBM is one of the core fronts of current AI chip controls. As the overseas HBM4 arms race intensifies, domestic HBM technology iteration is being pushed forward in sync — Huawei's Ascend roadmap has explicitly written "drive domestic HBM technology iteration" into its product cadence (the 950 series advances domestic HBM pairing, with the 960/970 series planned for gradual rollout in 2027–2028).

In the short term, HBM4 scarcity will directly transmit to the delivery cadence of Rubin / MI400; in the long term, whoever can lock in stable HBM4 supply holds the valve on 2027 AI compute expansion.

Related Links​

• NVIDIA Rubin Spec Page
• AMD MI455X Spec Page
• Hot Chips 2026 Full Recap

References​

• Samsung's HBM4 Yield Hits 80% as HBM Race Heats Up - TrendForce
• Samsung HBM4 Yield Hits 80%, Boosting AI Memory Supply - PC Central
• Samsung Weighs Shipping Legacy Memory Backend to Vietnam to Unlock HBM Capacity - TechTimes
• 芯闻|六个月良率跃迁：三星HBM4突破80%黄金门槛 - Toutiao

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This article is compiled from August 2026 public reports by TrendForce, Seoul Economic Daily, TechTimes, etc. HBM allocation shares and market shares are third-party estimates, not official vendor-confirmed data.

In 2026, China's AI chip market landscape has shifted from "NVIDIA unipolar dominance" to "overseas vendors leading, domestic multi-route catch-up." According to industry research, China's overall AI accelerator market was ~4M units in 2025, of which 1.65M were domestic, with share first breaking 40%; as products iterate and fabs follow up, the localization rate is expected to rise to 60%-70% by 2027. This article focuses on the latest H2 2026 progress of Huawei Ascend, Cambricon, and Moore Threads — the domestic "Big Three."

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1. Huawei Ascend: 950 Capacity Fully Booked, 960 Roadmap Unveiled​

Ascend's core advantage is "architecture + full-stack ecosystem synergy," with ~800K units shipped in 2025, capturing 50% of the total domestic vendor share. The product iteration cadence is clear:

Time	Product	Note
2025 Q1	Ascend 910C	Main transitional model
2026 Q1	Ascend 950PR	Inference flagship
2026 Q4 (planned)	Ascend 950DT	Training flagship, drives domestic HBM iteration
2027-2028	Ascend 960 / 970	Roadmap products

950 series capacity has entered a "fully booked" state: 950PR entered mass production in April 2026; June monthly capacity jumped to 500K-600K units (nearly 10x MoM), with a full-year target of 1.2M units at 100% certainty; ByteDance locked in 350K units for $5.6B, while Tencent / Alibaba / Baidu combined locked in 400K units.

Ascend 960 roadmap specs (per roadmap disclosure):

Metric	Ascend 960
Architecture	Ascend 6th gen (Da Vinci v6)
FP8 compute	~4 PFLOPS
Memory	288GB
Memory bandwidth	9.6 TB/s
Super-node	Atlas 960 SuperPoD, 15,488 cards, Lingqu optical-electrical converged bus
Debut	2027 Q4 (roadmap)

The previous-gen Ascend 384 super-node has cumulatively shipped over 750 sets, deployed across 20+ industries including internet, operators, finance, education, and healthcare — Huawei calls it "the only domestic super-node that has trained a SOTA model."

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2. Cambricon MLU690: H2 Mass Production, Entering ByteDance Bidding Window​

Cambricon is the core domestic compute leader in the absence of an Ascend IPO, with the technology gap continuously narrowing:

• Siyuan 590 (7nm): Performance equivalent to 80% of A100, already supports DeepSeek, continuously adapting to mainstream large models like Qwen 3 and GLM
• Siyuan 690 series: Will enter mass production in H2 2026, expected to achieve order scale-up during ByteDance's H2 bidding window
• Revenue certainty: Equity incentive targets show >100% revenue growth for the next 3 years: 2026 revenue target 13.5B RMB, 2027 27B RMB, 2028 60B RMB

Cambricon fully benefits from the industry dividend of "domestic CSP capex + full adaptation of domestic large models and domestic chips," making it the most direct elasticity play on rising localization rate.

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3. Moore Threads MTT S5000: Full-Function GPU + Ecosystem Breakthrough​

Moore Threads takes a differentiated "full-function GPU" route, with the flagship MTT S5000 based on the 4th-gen "Pinghu" MUSA architecture:

Metric	MTT S5000
Dense AI compute	1000 TFLOPS
Memory	80GB
Memory bandwidth	1.6 TB/s
Inter-card interconnect	784 GB/s
Precision	FP8 to FP64 full precision (training + inference)
Security	First batch to pass national "Safe and Reliable Evaluation" (Level I)

Its engineering capability is verified: the Kuae (KUAE) intelligent computing cluster based on S5000 achieves 95% training linear scaling efficiency, with compute efficiency loss within 5% at ten-thousand-card scale; supports checkpoint-resume training with effective training time ratio >90%; and has trained a MoE-236B base model with >25 trillion tokens of corpus from scratch.

The ecosystem is Moore Threads' deepest moat: MUSA has achieved 100% core math library compatibility, 3000+ PyTorch operator compatibility, covers 55 categories of core AI operators, has official vLLM and SGLang support, Day-0 adaptation of mainstream models, and 800K+ developers. Its PD heterogeneous-disaggregation solution achieves equivalent replacement of international high-end GPUs at a 2:1 ratio with S5000, significantly reducing inference cost.

The 5th-gen "Huagang" architecture (released 2025-12) supports FP4 to FP64 full precision, with 50% higher compute density and 10x better energy efficiency than the previous gen, supporting 100K+ card clusters; cumulative R&D investment in the "Huashan" (train-infer integrated) and "Lushan" (graphics rendering) new chips based on this architecture exceeds 900M RMB.

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4. Software Ecosystem Decides: Day-0 Adaptation Becomes Routine​

Beyond hardware, software ecosystem realization is the watershed for domestic compute in 2026:

• Huawei's CANN heterogeneous computing architecture and MindSeries suite are fully open-sourced, with the community incubating 67 projects, 12.44M+ lines of code, and 3,500+ monthly active developers
• The "release-and-adapt" closed loop between domestic large models and domestic chips has basically formed: Tencent Hunyuan T3 (295B), DeepSeek-V4, and GLM-5.2 all completed Day-0 adaptation
• 2026 is regarded as the "first year of domestic super-nodes"; Huatai Securities estimates China's super-node architecture market will reach 341.4B RMB by 2028, with a 2026-2028 CAGR of 194%

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5. Industry Judgment: From "Can It Be Built" to "Can It Be Used Well"​

The domestic Big Three are converging along three paths:

1. Huawei: Locks government/enterprise and internet big customers with super-node system-level capability + full-stack software
2. Cambricon: Impacts the revenue inflection point by narrowing the training-side gap + scaling up via big-customer bidding
3. Moore Threads: Covers cloud-edge-end full scenarios with full-function GPU generality + mature CUDA-compatible ecosystem

The common shortcoming of all three remains advanced process and HBM supply — precisely the core link of overseas controls. But as domestic HBM iterates and fabs follow up, a realistic path to 60%-70% localization by 2027 exists.

Related Links​

• Huawei Ascend 960 spec page
• Huawei Ascend 950DT spec page
• Cambricon MLU690 spec page
• Moore Threads MTT S5000 spec page

References​

• Domestic AI Chip Industry View Update - Robo Datayes
• Moore Threads, Two Positives in One Day - Eastmoney
• Moore Threads × SiliconFlow: PD Disaggregation Heterogeneous Compute Co-location Whitepaper - EE Focus
• After Approaching Break-even, Moore Threads Faces Hong Kong "Big Exam" Again - Top News

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This article is compiled from public industry research, broker views, and corporate announcements as of August 2026. Some shipment and market-share figures are third-party estimates, not officially confirmed data.

For the past three years, the entire AI hardware story was "training": who had the most H100s, who could connect a hundred thousand GPUs into a cluster. That race is essentially settled — NVIDIA won. But the next battlefield, "inference," is being fought under completely different rules: the measure is no longer peak FLOPS, but cost-per-token, latency, and power. In 2026, inference chips overtake training in scale for the first time, becoming the main battlefield of AI accelerators.

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1. Inference Becomes the Main Battlefield: 80%–90% of Compute Spent on Inference​

Training a large model costs hundreds of millions of dollars — once. But once the model goes live, it must answer billions of queries day after day. A popular consumer model may need tens of thousands of accelerators running 7×24 to keep up with demand. Therefore:

• Inference accounts for roughly 80%–90% of a model's lifecycle compute;
• Inference chips will make up about 60%–70% of the ~$400B AI accelerator market in 2026, up from only ~40% in 2023;
• Inference chip growth (estimated +52.7% YoY) significantly outpaces training chips (+28.4%); the share of inference-side compute demand exceeded training-side for the first time in 2026, reaching 54% (~$1010B).

The economics of inference are straightforward: training cost is amortized to near-zero, while inference cost becomes the entire bill. Every 1% cut in inference cost flows directly to profit — for a company whose inference traffic reaches hyperscale like OpenAI, the half of the bill is a number followed by a string of zeros.

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2. Market Size: Structural Growth Inflection Point Has Arrived​

Market	2026 Size	Growth	Notes
Global dedicated inference chips	$412.7B	+38.4%	14.2 pct higher growth than training chips
China dedicated inference chips	$118.6B (28.7% of global)	+44.1%	Strongest single market in APAC by growth
Global AI training/inference chips (incl. GPU/NPU)	exceeds $1850B	+40.2%	GPU ~62%

China's domestic substitution is accelerating, with domestic inference chips reaching 34.6% of shipments, up 9.8 pct from 2025.

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3. Technology-Axis Share Inverts: GPU Slows, ASIC Soars​

Axis	2026 Shipment Share	Trend
GPU	52.6%	Still leads, but growth slows to 22.7%
ASIC custom chips	41.3%	Up sharply from 17.8% in 2022
FPGA	Stable	Specific low-latency scenarios

Thanks to ecosystem maturity, GPU remains the mainstay, but NPU/ASIC already holds a 1.8× advantage over same-generation GPUs in energy efficiency, driving rapid adoption at the edge and on-device. Shipments of inference-optimized ASICs are expected to reach 11.5 million units, with unit cost about 35% lower than GPUs.

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4. Five Schools Clash​

School	Representative Products	Core Strength	Use Cases
General-purpose GPU	NVIDIA Rubin / B200 / H200	Mature ecosystem, train+infer unified	Frontier training + highly interactive inference
LPU (Language Processing Unit)	Groq LPU	Ultra-low latency, deterministic throughput	Real-time dialogue, high-concurrency inference
TPU (inference-specific)	Google TPU 8i (Zebrafish)	288GB HBM, 384MB on-chip SRAM, 19.2 Tb/s ICI	Google's scaled inference
Custom ASIC	OpenAI Jalapeno, Microsoft Maia 200, Meta MTIA	Strip generality tax for own models, ~50% lower cost/token	Hyperscaler's own workloads
Air-cooled inference card	Intel Crescent Island	350W air-cooled, 480GB LPDDR5X, tokens/watt	Cost-sensitive mid/long-tail inference

OpenAI's Jalapeno, co-developed with Broadcom, aims to cut inference token cost by roughly 50% versus a general-purpose GPU stack — the fifth member to join the "custom inference chip club" (after Google TPU, Amazon Inferentia/Trainium, Microsoft Maia, and Meta MTIA).

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5. Core Metric Shifts: cost-per-token and tokens/watt​

The fundamental difference between the inference race and the training race is the low switching cost:

• Training requires a 100k-GPU cluster + NVLink + CUDA, with extremely high migration cost;
• Inference is "embarrassingly parallel" at the endpoint level — no million-GPU cluster needed; a node that produces tokens fast and cheaply suffices, and is replaceable per endpoint.

This means NVIDIA's three moats (fastest silicon, NVLink scale-out, CUDA) are no longer absolute on the inference side. When the largest AI buyer (OpenAI) starts treating GPUs as "one of the options," the GPU premium begins to erode — pricing power relies on scarcity, and custom chips attack that scarcity from two directions at once: both reducing merchant-chip demand and giving buyers a credible external negotiation option.

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6. Edge and On-Device Explosion: Long-Tail Signal​

Demand shows significant long-tail and fragmentation:

Scenario	2026 Demand Size	Growth
Cloud inference	$198.2B (48%)	+24.5% (slowing)
Edge inference	$126.5B (30.7%)	+52.3%
On-device inference	$88.0B (21.3%)	+68.9%
Autonomous-driving inference	$67.3B	+58.2%
Industrial QA / robotics inference	$42.1B	+63.7%

The latency sensitivity and power constraints of inference workloads are reshaping chip architecture design priorities — which also explains why "air-cooled, large-memory" solutions like Crescent Island can find a niche.

Related Links​

• Hot Chips 2026 Full Recap
• Hyperscaler Custom Silicon Wave 2026
• NVIDIA Rubin Spec Page
• Intel Crescent Island Spec Page

References​

• Inference Chips vs Training Chips - ValueAdd VC
• Global and China Dedicated Inference Chip Industry In-Depth Research Report (2026) - IIM
• 2026 Global AI Training/Inference Chip Market and Trend Report - IIM
• Custom AI inference silicon: why OpenAI built Jalapeno - nexi.fund

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This article is compiled from publicly available 2026 market research, brokerage reports, and industry analysis. Market sizes and shares are third-party estimates with inconsistent methodologies and are for reference only.

The 2026 World Artificial Intelligence Conference (WAIC) was held July 17-20, 2026 at the Shanghai World Expo Center, themed "Intelligent Partners, Creating the Future Together." Over 1,100 companies showcased 3,000+ exhibits, with 300+ products debuting globally. For the compute-card industry, this concentrated review of domestic compute sent a clear signal: the competitive main line is shifting from "single-chip peak compute" to "SuperNode system-level effective compute."

1. Huawei Atlas 950 SuperPoD: live debut, wins SAIL grand award​

Huawei's Atlas 950 SuperPoD live hardware made its first public appearance at WAIC 2026, on-site carrying 16 compute cabinets with 1,024 Ascend cards total. With three system-level innovations — "ultra-wide bandwidth, ultra-low latency, unified memory addressing" — it stood out from hundreds of domestic and international entries to win the conference's top honor, the SAIL (Super AI Leader) Award.

Core parameters (confirmed on-site at WAIC)​

Metric	Atlas 950 SuperPoD
Exhibited scale	16 compute cabinets / 1,024 Ascend cards
Max interconnect scale	8,192 Ascend NPU cards fully interconnected (full config)
Interconnect protocol	Huawei in-house "Lingqu" (UnifiedBus) 2.0
Total compute	1 EFLOPS FP8 / 2 EFLOPS FP4 (1,024 cards); full 8,192-card ~8 EFLOPS FP8
Unified memory	256 TB globally unified memory address space
Interconnect latency	3 μs ultra-low RTT; TB-level NPU interconnect bandwidth
Full config	128 compute cabinets + 32 interconnect cabinets = 160 cabinets, ~1000㎡, carrying 8,192 Ascend 950DT
Launch	Full config planned for Q4 2026
Cooling	Fully liquid-cooled blind-plug architecture

Huawei disclosed for the first time: the previous-gen Ascend 384 SuperNode has cumulatively shipped 750+ units commercially, deployed across 20+ industries including internet, operators, finance, education, healthcare, transportation, and manufacturing, calling it "the only domestic SuperNode that has trained SOTA models."

2. Software ecosystem: CANN fully open-sourced, developers at scale​

Beyond hardware, Huawei highlighted open-source software ecosystem progress:

• CANN heterogeneous compute architecture and MindSeries base software suite were fully open-sourced end of 2025;
• The CANN open-source community has incubated 67 projects, 12.44M+ lines of code, with 3,500+ monthly active developers;
• Huawei has co-developed 7,000+ solutions with 3,000+ industry partners, serving 2,000+ core government/enterprise customers;
• WAIC showcased 60+ real business scenarios, 20+ benchmark cases, covering the full chain from technology breakthrough to scaled commercial deployment.

3. Domestic chips' Day-0 adaptation becomes routine​

On July 6, 2026, Tencent released the MoE model Hunyuan T3 (295B parameters, 256K context); domestic chips rapidly completed Day-0 adaptation:

Vendor	Chip	Adaptation status
Moore Threads	MTT S5000	Completed rapid Hunyuan T3 adaptation (previously adapted DeepSeek-V4, GLM-5.2)
MetaX	Xiyun C series	In-house MXMACA stack first to full-chain Day-0 adaptation, zero-code deployment

Moore Threads also showcased the MTT C256 SuperNode (first-of-its-kind single-layer Scale-up 256-card full interconnect, sub-microsecond latency) and three AI-factory solutions — "model training factory / token production factory / agent production factory."

4. More domestic compute debut highlights​

Vendor / product	Highlight
Orient AlphaChip DF1000	World's first "software-defined + near-memory computing" 3D chip, interconnect pitch compressed to sub-micron
ZhongHao XinYing "Xuyu"	Fully in-house next-gen TPU-architecture AI-specific chip, with Taize 2.0 server
Enflame × Iluvatar	Domestic high-performance Matrix SuperNode based on OEX+dOCS architecture, shortlisted for the conference "Excellent AI Leader Award"
Rongming Microelectronics	Advancing next-gen VPU, evolving from video processing to "visual-agent compute base"

The domestic AI chip lineup also included Moore Threads, MetaX, Enflame, Houmo, Cixiong, Suaneng, SemiDrive, Phytium, Aixin, Iluvatar, and others.

Industry interpretation: from "can it be built" to "is it used well"​

WAIC 2026 reflects a fundamental shift in the competitive stage of domestic AI chips:

1. SuperNode becomes the main battlefield: beyond single-chip performance, system-level capabilities — "inter-chip interconnect + cluster scale + cooling" — become the breakthrough key. Huawei Lingqu and Enflame/Iluvatar OEX are both pushing here. Huatai Securities defines 2026 as the "first year of domestic SuperNodes," estimating China's SuperNode architecture market could reach ¥341.4B by 2028, with 2026-2028 CAGR of 194%.
2. Software ecosystem delivers: Day-0 adaptation has gone from slogan to routine; the "launch-and-adapt" closed loop between domestic large models (DeepSeek-V4, GLM-5.2, Hunyuan T3) and domestic chips is essentially formed.
3. Demand-side endorsement: China Mobile earlier released its 2026-2027 AI SuperNode centralized procurement announcement — about 6,208 cards, over ¥2B — accelerating domestic SuperNode scaled commercialization.

Related links​

• Huawei Ascend 950DT specs
• Huawei Ascend 950PR specs
• Moore Threads MTT S5000 specs

References​

• Ascend 950 SuperNode live at WAIC 2026, wins SAIL top award - Global Times
• 2026 WAIC compute competition enters "system-level" battlefield, Ascend 950 SuperNode debuts - Tencent News
• Huawei unveils advanced Atlas 950 SuperPoD - China Daily
• Huawei Ascend 950 wins WAIC 2026 SAIL top honor - Huxiu
• Compute heavyweights at WAIC: Huawei Ascend 950 SuperNode live - 21st Century Business Herald

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This article is compiled from WAIC 2026 (July 17-20) on-site and official disclosures, and will continuously track the 950 SuperNode Q4 launch.

From December 2025 to July 2026 — just half a year — at least 6 AI chip companies have listed or are about to list on capital markets. Together with already-listed Cambricon, Hygon, and Iluvatar, the domestic GPU corps' total market cap is approaching ¥2 trillion. This marks the critical climb from domestic GPUs being "usable" to "useful."

1. The "Four Little Dragons" assemble on capital markets​

Company	Listing status	Raise / issue price	Sponsor
Moore Threads	Listed (STAR Market sh688795, 2025-12-05)	Issue price ¥114.28, raised ¥8B	CITIC Securities
MetaX	IPO accepted (2026-06-30)	¥3.904B (total investment ¥5B)	Huatai United
Enflame	Passed review (2026-06-15)	¥6B	—
Biren	HKEX / sprinting	—	—

Already-listed camp: Cambricon (sh688256, STAR Market 2020-07-20), Hygon, Iluvatar (HKEX). Moore Threads turned a book profit of ¥29.35M in Q1; MetaX narrowed losses 57.7% and gave a 2026 breakeven timeline.

2. Moore Threads MTT S5000: benchmarking against H100​

Moore Threads announced its flagship AI train+inference GPU MTT S5000 successfully completed full-pipeline adaptation validation of Zhipu's new-generation large model GLM-5 — measured performance "breaks the domestic compute ceiling":

Metric	MTT S5000
Architecture	4th-gen "Pinghu" architecture
FP8 compute	1 PFLOPS (1,000 TFLOPS)
Memory bandwidth	1.6 TB/s
Positioning	Full-function train+inference GPU, benchmarks against NVIDIA H100
Production	Mass-produced; clusters online supporting trillion-parameter training

Deployment validation: jointly completed full-pipeline training of embodied-brain model RoboBrain 2.5 with BAAI; partnered with SiliconFlow for high-performance DeepSeek-V3 inference, single-card speed near international top products. IPO funds go to three directions: next-gen AI train+inference chip, next-gen graphics chip, next-gen AI SoC chip.

WAIC 2026 new progress: Moore Threads showcased the MTT C256 SuperNode (first-of-its-kind single-layer Scale-up 256-card full interconnect, sub-microsecond latency) and three AI-factory solutions — "model training factory / token production factory / agent production factory"; the company pre-announced H1 2026 revenue of ¥1.65B-1.75B, up 135%-149% YoY.

3. Cambricon: dual flagships MLU590/690​

Chip	Process	Compute	Memory	Customer / status
MLU590 (思元590)	7nm Chiplet	INT8 512 TOPS / FP16 345 TFLOPS	96 GB HBM2e	ByteDance inference mainstay, ~80% of A100 overall, mass shipments early 2026
MLU690 (思元690)	5nm-class (SMIC N+2)	FP16 700+ TFLOPS / INT8 2800+ TOPS	196 GB HBM3 (3.35 TB/s)	Dual-die packaging, MLU-Link 890 Gbps; ~70% of H100 (80-90% pure inference); ByteDance largest customer, mass production early 2026

Cambricon is the only domestic AI chip vendor with a "unified edge-cloud architecture" — one MLU instruction set spans 思元 220 (edge) → 370 (border) → 590/690 (cloud), with one NeuWare toolchain across compute tiers.

Capital and performance double explosion: Cambricon's total market cap exceeded ¥1 trillion on June 30, 2026, becoming the STAR Market's first "trillion-yuan stock," up 75%+ YTD. On performance, Q1 2026 revenue ¥2.885B (+160% YoY), deducted net profit ¥934M; full-year 2025 revenue ¥6.497B (+453% YoY), net profit attributable to parent ¥2.059B, ending long-term losses. ByteDance has cumulatively deployed over 100k 思元 590/690, its largest customer.

4. DeepSeek-V4 effect: changing the expectation coordinate system​

On April 24, 2026, DeepSeek released the trillion-parameter flagship DeepSeek-V4. Unlike a year earlier when V3's launch sparked debate over "can domestic chips even run large models," this time multiple domestic chips — Huawei Ascend, Cambricon, Hygon, MetaX, Moore Threads, Kunlun, T-Head, Iluvatar — completed adaptation on launch day.

The evaluation coordinate system is shifting: from "what percentage of NVIDIA's same-generation product performance" to "can it carry the real workloads of top-tier large models."

Industry interpretation​

1. Capital ammunition in place: dense IPOs provide ample funding for domestic GPU R&D iteration and capacity expansion, moving from "technology breakthrough" to "commercial virtuous cycle."
2. Train+inference becomes the mainstream route: Moore Threads takes the full-function GPU route (graphics+AI+general compute), differentiating from Huawei Ascend's "AI-focused."
3. Software ecosystem is the decider: Day-0 adaptation and the maturity of unified software stacks (MUSA / NeuWare / MXMACA) are replacing raw peak compute as the core yardstick of domestic GPU "usability."

Related links​

• Moore Threads MTT S5000 specs
• Cambricon MLU590 (思元590) specs
• Cambricon MLU690 (思元690) specs

References​

• Moore Threads flagship GPU benchmarked against NVIDIA H100 - Anue
• GPU Four Little Dragons take the stage, Cambricon no longer alone - Sina Finance
• GPU industry status and development trends (2026) - 10jqka
• 2026 domestic AI compute tracker (Huawei Ascend, Cambricon, Moore Threads)

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This article continuously tracks the domestic GPU listing process and product iteration.

The Ascend 950 series (950PR inference / 950DT training) has become the core supply of domestic AI compute. Per multiple brokerages and industry research, 950 series capacity is 100% booked with scarce spot supply; the full-year 1.2M-unit target is "100% certain," with expectations of an upward revision to 1.5M. This article summarizes capacity and order data as of July 2026.

1. Capacity pace: ~10× MoM jump in June​

Time	950PR monthly capacity	Notes
May 2026	50k-60k units	Near full production
June 2026	500k-600k units	~10× MoM; SMIC, Hua Hong tier-1 suppliers on overtime
Q3 2026 (est.)	700k-800k units	Per month
Full-year 2026 target	1.2M units	Upward revision to 1.5M expected

Supply chain delivery is tight: high-speed backplanes and liquid-cooling connectors' lead time stretched from 2 weeks to 6-8 weeks; orders are booked into 2027.

2. Order structure: top cloud providers + operators + overseas​

Customer	Locked volume	Amount / Notes
ByteDance	350k 950PR	$5.6B, concentrated delivery from Q3 2026
Tencent / Alibaba / Baidu	~250k 950PR + 150k 950DT	Combined ~400k units
Three major operators	200k+ units	Centralized procurement, for intelligent compute centers and AI private networks
Overseas	South Korea 2,000 units, Malaysia 3,000 servers, Russia ten-thousand-card cluster	From pilot to commercial

3. Shipment forecast: firmly #1 domestic​

Per CCA (Kezhi) Consulting estimates:

Metric	2025	2026 (forecast)
Huawei Ascend total shipments	812k cards	1.026M cards
Of which 950PR	—	~800k units
Of which 950DT	—	~100k-200k units

Huawei has completed the product transition from the 910 series to the 950 series. The internet industry has become Ascend's largest application market; competitive advantage is extending from single-hardware performance to software ecosystem and system capabilities.

4. Going overseas: formal South Korea entry in Q4​

Per Korean media ETNews, Huawei plans Q4 2026 to formally enter the South Korean market with the Ascend series and Atlas 950 SuperPod:

• Local distributor agreements signed; two channel partners including SK Shieldus selected
• Main products: 950PR (mass-produced and delivered since April) and 950DT (launched Q4)
• Official line: 950PR inference performance is 2.87× that of H20, priced at about 1/4 of it

5. WAIC 2026: 1024-card live debut confirmed​

At WAIC 2026 (July 17-20, Shanghai), Huawei's Atlas 950 SuperPoD live hardware made its first public appearance — a 16 compute-cabinet, 1,024 Ascend-card scale — and won the conference's top honor, the SAIL Award:

• Core metrics: total compute 1 EFLOPS FP8 / 2 EFLOPS FP4, 256 TB globally unified memory addressing, Lingqu 2.0 interconnect, 3 μs ultra-low RTT latency
• Full configuration: 128 compute cabinets + 32 interconnect cabinets = 160 cabinets, ~1000㎡ footprint, carrying 8,192 Ascend 950DT, planned Q4 2026 launch
• Commercial foundation: previous-gen 384 SuperNode has cumulatively shipped 750+ units, deployed in 20+ industries
• Software ecosystem: CANN fully open-sourced end of 2025; community incubated 67 projects, 12.44M+ lines of code, 3,500+ monthly active developers

WAIC's debut confirmed the 950 series' "SuperNode-first" product logic: beyond single-card compute, system-level effective compute (interconnect bandwidth + unified memory + low latency) is the key dimension for domestic compute to benchmark against international flagships.

Ascend roadmap recap​

Product	Positioning	Key metrics (official roadmap)
950PR	Inference	1 PFLOPS (FP8) / 2 PFLOPS (FP4), 2 TB/s interconnect
950DT	Training	SuperNode core, launched Q4
960	Train/inference	2 PFLOPS (FP8) / 4 PFLOPS
970	Next-gen	In planning

Industry interpretation​

1. Domestic substitution moves from inference to training: 950PR (inference) ramps first, 950DT (training) follows in Q4, combined with the Atlas 950 SuperPoD ten-thousand-card interconnect — domestic compute now has the complete "training substitution" puzzle for the first time.
2. Capacity is the biggest variable: order certainty is extremely high, but SMIC/Hua Hong advanced-process capacity, HBM supply, and advanced packaging remain ramp bottlenecks — the root of "scarce spot supply."
3. Going overseas opens a second growth curve: bulk procurement from South Korea, Malaysia, Russia, and Latin America marks domestic compute's shift from "internal circulation" to "external circulation."

Related links​

• Huawei Ascend 950PR specs
• Huawei Ascend 950DT specs
• Huawei Ascend 950 specs

References​

• China intelligent-compute chip competitive landscape (CCA data) - IDCquan
• Ascend 950 series capacity and orders - Eastmoney
• WAIC catalyst: Huawei Ascend 950 SuperNode live debut - 10jqka
• Huawei Ascend 950 enters South Korea in Q4: 2.87× H20 inference - Toutiao

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Data in this article is based on official and major brokerage research; capacity/orders are dynamic figures and will be continuously updated.

July 2026, NVIDIA's next-gen AI compute platform Vera Rubin officially began its first shipments, succeeding the Blackwell architecture, with large-scale mass production planned for H2 2026. First customers include Microsoft, Google, Amazon, Meta, Oracle, and other large cloud providers.

1. World's First VR200 NVL72 Delivered (Milestone)​

CoreWeave jointly with Dell announced that the world's first NVIDIA Vera Rubin VR200 NVL72 cabinet has been officially delivered and passed the L11 full-cabinet hardware diagnostics on the first try. This marks Rubin's move from roadmap to physical product, with no major bottlenecks in core supply-chain links (HBM4, advanced packaging, liquid cooling, ultra-high-power power supply).

VR200 NVL72 Core Configuration​

Metric	Vera Rubin VR200 NVL72
Cabinet codename	Oberon
GPU	72 Rubin GPUs
CPU	36 Vera CPUs
Per-GPU memory	288 GB HBM4
Per-CPU memory	1.5 TB LPDDR5X
Total cabinet HBM4	20.7 TB (20,736 GB)
Total cabinet LPDDR5X	54 TB
Interconnect	NVLink 6 full mesh
Inference performance	~3.6 exaFLOPS class
Cooling	Liquid cooling
Generational improvement	~3.5× per-GPU compute, ~2.8× memory bandwidth (vs Blackwell)

Vera CPU integrates 88 custom Olympus ARM cores, with 1.8 TB/s interconnect to the GPU, usable as a GPU memory expansion pool. NVIDIA completed its first Vera CPU deliveries to Anthropic, OpenAI, xAI, and Oracle Cloud in May.

2. Samsung HBM4 Mass Production: Key Bottleneck Eases​

July 8, 2026, Samsung Electronics officially started HBM4 mass production for the Vera Rubin platform, with reported HBM4 mass-production yield reaching 70% (above the initial 60-65% expectation). Confirmation of this key supply-chain link clears obstacles for Rubin's large-scale deployment.

HBM Supply Landscape (2026 Q1)	Share
SK hynix	45%
Samsung	40%
Micron	15%

HBM4 uses 8-layer stacking (12-layer design planned for 2028), priced at about 2.8× HBM3e. TrendForce predicts HBM supply will grow 65% annually, with HBM4 reaching 35% of total output by 2027 Q4.

3. Rubin Ultra Sky-High Price: HBM Cost Dominates​

Per BofA Global Research estimates, the Rubin generation will push single-server cost to a new high:

Cost Item	Rubin VR200 (Oberon)	Comparison
Cabinet HBM4 usage	20,736 GB	—
HBM4 unit price	~$18.40 / GB	Blackwell (HBM3e) ~$11.26 / GB
HBM4 cost alone	~$382K	Excluding LPDDR5X
Rubin Ultra cabinet estimated price	~$21M	ITHome / BofA estimate

4. Rubin Ultra Design Change: Original 4-die Cancelled (per SemiAnalysis)​

Semiconductor research firm SemiAnalysis (2026-06-30) disclosed that the original 4-die Rubin Ultra GPU unveiled at GTC 2026 has been cancelled; the version actually shipping in 2027 is roughly halved in scale and performance:

• Reason for cancellation: The original integrated 4 compute dies + 16 HBM4E in a single CoWoS-L package; the substrate warped under the 4-die config, causing compute-die-to-substrate contact failure and yield collapse; the alternative CoPoS won't reach mass production until after late 2028, missing the 2027 node.
• New approach: Changed to dual-die (same construction as standard Rubin) + HBM4E, ~384 GB HBM4E per GPU (higher than standard Rubin's 288 GB), but total compute and bandwidth only half the original; to approach the original's aggregate compute, NVIDIA plans to assemble "2+2" board-level configs within the Kyber rack to reach four-die equivalent scale.
• Kyber rack delay: The companion Kyber NVL144 rack is delayed 12+ months to 2028 due to midplane PCB manufacturing difficulties; the 800V DC power scheme is likewise delayed to 2028.

⚠️ Note: NVIDIA has not commented officially on the above design change; some on X argue "the chip count hasn't changed, it's old news reheated." This section is compiled from SemiAnalysis public reports, subject to final NVIDIA disclosure. We have marked "specs pending official confirmation" on the Rubin Ultra preview card.

Industry Interpretation​

1. "Never doubt" moment realized: Rubin's first delivery passed L11 on the first try, dispelling market doubts about "Rubin delay," locking in H2 2026 AI compute supply certainty ahead of time.
2. Designed for Agentic AI: Rubin targets agentic workflows and ultra-long-context inference, further lowering the training/inference cost curve for trillion-parameter models.
3. HBM is the full-chain winner: 20.7 TB HBM4 per cabinet is enormous usage; SK hynix, Samsung, Micron, advanced packaging (CoWoS-L), liquid cooling, and power retrofitting all benefit across the chain, while also becoming the biggest cost and capacity constraint.

Related Links​

• NVIDIA Rubin R200 spec page
• NVIDIA Vera CPU spec page
• NVIDIA Rubin CPX spec page

References​

• Samsung Starts Mass Production for Nvidia's Vera Rubin AI Platform (2026-07-08)
• Rubin cabinet first delivery: CoreWeave and Dell deliver world's first VR200 NVL72
• Nvidia Rubin Ultra cabinet estimated at $21M - ITHome (2026-07-09)
• NVIDIA Vera Rubin scheduled for July 2026 shipment - VendorDeep

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This article continuously tracks Vera Rubin mass-production ramp and HBM4 supply-chain dynamics.

In the first half of 2026, the AI chip industry underwent a historic turning point — the center of gravity shifted from the "training race" to "inference efficiency," domestic chip market share broke 40% for the first time, NVIDIA built higher barriers with Blackwell Ultra, and the inference-specific chip track bloomed in diversity.

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I. Compute Doubles Again: NVIDIA Blackwell Ultra Launch (June 1)​

On June 1, 2026, NVIDIA CEO Jensen Huang unveiled the new-generation AI chip Blackwell Ultra at Computex 2026 (Taipei), setting a new starting line for the AI infrastructure race over the next two years.

Key Specs​

Metric	Blackwell Ultra	B200	Improvement
FP8 compute	20 petaFLOPS	~10 petaFLOPS	100%
Architecture	Blackwell Ultra	Blackwell	Upgrade
Expected delivery	2027 Q1	2026 Q1	—
Positioning	Hyperscale training + inference	Training + inference	Flagship

Industry Significance​

1. Direct impact of doubled compute: 20 petaFLOPS FP8 means training time for hundred-billion-parameter models drops sharply; trillion-parameter model training moves from "scientific experiment" to "engineering routine"
2. System-level balance: Blackwell Ultra is not just a chip but a system-level engineering breakthrough across NVLink, HBM, cooling, and power delivery
3. Roadmap certainty: The Q1 2027 delivery timeline lets cloud vendors and AI labs plan infrastructure budgets 18 months ahead

Challenges​

• Energy crisis: Doubled performance comes with sharply higher power; datacenter power and cooling design face extreme challenges
• Accessibility: Top-tier compute goes first to top cloud vendors; how smaller developers and research institutes reach compute at reasonable cost via cloud services
• Software stack adaptation: New hardware needs matching CUDA versions and framework support; software ecosystem maturity becomes the key bottleneck for compute conversion

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II. Domestic AI Chips: The Tipping Point from "Usable" to "Good"​

On June 16, 2026, Xinchuang World published "2026 China Domestic AI Chip Vendor Capability Quadrant", clearly outlining the overall domestic landscape.

2.1 Capability Quadrant Ranking​

Quadrant	Representative Vendors
Leader quadrant	Huawei Ascend, Hygon, Cambricon, Alibaba T-Head, Moore Threads
Visionary quadrant	Baidu Kunlunxin, Biren, Enflame, Iluvatar, HardyVision
Contender quadrant	TSINGMICRO, Black Sesame, SemiDrive, Lisuan, Houmo
Challenger quadrant	Denglin, Zhicun, VeriSilicon, Rockchip, Intellifusion

2.2 Huawei Ascend: The Anchor of Domestic Compute​

Market Position​

• In 2025, Ascend series shipped 812,000 units, capturing 49% of the domestic AI accelerator card share, firmly No.1 domestically
• Ascend 950PR single-card FP8 compute reaches 1P (PetaFLOPS), FP4 compute reaches 2P
• Inference performance is about 2.87x that of NVIDIA H20, priced at only 72,000-75,000 RMB, a significant price/performance advantage

Full-Stack Advantage​

Huawei's "device-network-cloud-chip" integrated strategy is Ascend's core moat:

• Chip design: Da Vinci 3.0 architecture iterating continuously
• OS: HarmonyOS/Euler OS deeply optimized
• Networking: Euler network protocol stack
• Cloud: Huawei Cloud ModelArts platform seamlessly integrated

Latest Progress​

• On June 5, 2026, Shenzhen Hetao College, together with HIT (Shenzhen) and Huawei, completed full-parameter post-training of a 1.6-trillion-parameter DeepSeek V4 Pro model on an Ascend 910C cluster
• This is the first time domestic AI chips completed trillion-parameter-level model training, marking "domestic substitution" moving from inference to training

2.3 Cambricon: The First Profitable Domestic AI Chip Benchmark​

Performance Explosion​

Metric	Full-year 2025	2026 Q1	YoY Growth
Revenue	6.497B RMB	2.885B RMB	+453% / +160%
Net profit	2.059B RMB (first annual profit)	1.013B RMB	— / +185%

Core Product: Siyuan 590​

• In DeepSeek R1 inference scenarios, TPS reaches 942, about 50% higher than H20
• Years of joint optimization with ByteDance; strongest short-term cloud inference deployment capability
• Of 2.885B RMB Q1 2026 revenue, Siyuan 590 contributed over 70%

Potential Risks​

Absent from the 2nd 2026 "Safe and Reliable Evaluation Results Announcement"; the reason is unclear and will affect its domestic government/enterprise market performance.

2.4 TSINGMICRO: The "Third Route" of Reconfigurable Chips​

Technical Route​

TSINGMICRO adopts a reconfigurable dataflow architecture同源 with Groq LPU, finding a balance between GPU generality and ASIC extreme efficiency.

Metric	TSINGMICRO TX81	Traditional GPU	Advantage
Inference cost	Baseline	+100%	Reduced 50%
Energy efficiency	Baseline	Baseline	3x improvement
Architecture	Reconfigurable dataflow	SIMT/SIMD	Better for inference

Deployment Progress​

• Cumulative shipments of reconfigurable chips exceed 30 million units
• Scaled deployment in a dozen-plus thousand-card-scale intelligent computing centers nationwide
• Has begun A-share IPO tutoring; likely to become the "first reconfigurable chip stock"

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III. The Inference Chip Track: Core Signal of the Industry Shift​

On June 4, 2026, TrendForce published a deep report "The Era of Inference Economy: The Rules of AI Chips Are Being Rewritten," pointing out that the compute competition center of gravity is shifting from training to inference.

3.1 Why Now?​

Cost Structure Changed​

• Training is a one-time cost: Once a model is trained, marginal cost approaches zero
• Inference is a recurring cost: Every API call, every generated token represents compute consumption and gross-margin pressure
• Per-unit inference cost and energy efficiency directly affect gross margin and scale-expansion capability

Model Compression Tech Matured​

• 1.58-bit quantization and weight pruning let models maintain inference accuracy at extremely low memory footprint
• MoE (Mixture of Experts) architecture activates only a few expert sub-networks per inference via "partial wake-up," greatly reducing actual computation
• The rise of slimmed models provides commercial viability for hard-wired inference chips

3.2 NVIDIA's $20B Bet: Acquiring Groq (December 2025)​

On December 24, 2025, NVIDIA acquired Groq's inference technology license and core team for $20 billion, one of NVIDIA's largest M&A/tech acquisitions ever.

Strategic intent:

1. Fill the inference gap: NVIDIA GPU is unshakable in training, but inference efficiency was never its strongest suit
2. Counter specialized inference chips: Cerebras, Taalas, SambaNova and other startups are eroding the inference market
3. Position for Agentic AI: Agentic AI needs extremely low-latency, high-throughput inference

3.3 Taalas HC1: Proof of Concept for Hard-Wired Inference​

On February 20, 2026, Canadian AI chip startup Taalas launched inference chip Taalas HC1, directly etching Meta's open-source AI model Llama 3.1 8B into the chip.

Key Metrics​

Metric	Taalas HC1	NVIDIA B200 (throughput optimized)	Advantage
Inference rate	16,960 tokens/s/user	Baseline	~4-5x
Cost per million tokens	0.75 cents	3.79 cents	Reduced 80%
Power	~250W	~700W	Reduced 64%
Process	TSMC N6	TSMC 4nm	More mature
HBM	❌ Not used	✅ HBM3e	Lower cost

Technical Principle​

Taalas HC1 uses an aggressive Computing-in-Memory (CIM) implementation:

• Model weights directly固化 in Mask ROM (fully hardware-defined)
• On-chip SRAM handles dynamic data (KV cache and LoRA fine-tuning weights)
• Only 2 mask layers need modification to produce a dedicated chip for another AI model; turning an AI model into a physical chip takes only 2 months

Limitations​

• Lack of flexibility: Hard-wiring cannot cope with rapidly iterating model updates
• Ecosystem barrier: The current cloud market still relies on general-purpose platforms; customers may prefer flexible solutions that upgrade with models
• NRE cost: High one-time engineering cost, requiring sufficient deployment scale to amortize

3.4 Cerebras: The IPO Path of Wafer-Scale Integration​

On May 14, 2026, Cerebras Systems officially listed on NASDAQ, becoming the first wafer-scale AI chip company to go public.

Core Technology: Wafer-Scale Integration (WSI)​

• WSE-3 (third-gen wafer-scale engine): An entire 12-inch wafer as a single chip
• 44GB on-chip SRAM: No external HBM, eliminating the memory bandwidth bottleneck
• 21 PB/s bandwidth: On-chip communication bandwidth, thousands of times that of GPUs
• Partnership with OpenAI: Signed a 3-year, 750MW, $20B+ compute cooperation agreement

IPO Significance​

Cerebras's listing marks the maturation of the inference-specific chip track:

1. Capital markets begin pricing such companies
2. Proves "non-GPU" technical routes have commercial viability
3. Provides valuation references for other inference chip startups (Groq, SambaNova, Taalas, etc.)

3.5 Inference Chip Landscape: Multiple Technical Routes Coexist​

Company	Technical Route	Core Advantage	Representative Product
Taalas	Hard-wired (Mask ROM)	Extreme inference efficiency, low cost	HC1
Cerebras	Wafer-scale integration (WSI)	Ultra-high bandwidth, large-model inference	WSE-3
Groq	SRAM-first architecture	Deterministic latency, high throughput	LPU (acquired by NVIDIA)
d-Matrix	Digital in-memory compute (DIMC)	More flexible than hard-wiring	Corsair
Etched	Hard-wired Transformer	Works for all Transformer models	Sohu
Axelera AI	Digital in-memory compute (D-IMC) + RISC-V	High energy efficiency	Metis AIPU

TrendForce predicts:

• General-purpose GPUs still dominate training and multi-model environments
• But in mature, predictable scenarios, general-purpose GPU profit margins will be compressed
• The industry shifts from general compute monopoly to a dual-track structure of general + specialized coexistence

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IV. Overall Domestic AI Chip Landscape in H1 2026​

4.1 Industry Enters Scale-Up Phase​

Metric	2025	2026 Q1	Trend
Domestic AI accelerator shipments	1.65M units (41% share)	—	Rising
Total China AI accelerator shipments	~4M units	—	—
Hygon revenue growth	—	Doubled	↑
Cambricon revenue growth	—	+160%	↑
Moore Threads revenue growth	—	Doubled	↑

Leading vendors collectively entered the revenue realization channel, moving from "technical validation" to "scale commercialization."

4.2 Three Core Development Trends​

Trend 1: Capitalization Wave Reshapes the Landscape​

• Late 2025 to early 2026: Moore Threads, Iluvatar listed on the STAR Market
• Biren listed on the Hong Kong stock exchange
• Enflame STAR Market IPO accepted
• Kunlunxin, T-Head initiated listing processes
• TSINGMICRO, HardyVision and others advancing IPOs

Capitalization brings dual effects:

• ✅ Positive: Supports R&D and ecosystem building
• ⚠️ Negative: Valuation bubbles and revenue realization pressure

Trend 2: Capacity Becomes the Biggest Constraint Variable​

The contradiction between explosive domestic AI chip demand and limited advanced-process capacity is sharpening:

Vendor	Advanced-process capacity need	Actually obtained
Huawei Ascend	15K wafers/month (7nm-class)	Priority guaranteed
SMIC total capacity	~20K wafers/month (7nm-class)	—
Other vendors	~5K wafers/month combined	Extremely tight

Whether stable wafer capacity can be secured directly determines vendor survival. Cambricon's 75.4% inventory-to-revenue ratio is essentially a lock on capacity.

Trend 3: Competition Shifts from "Usable" to "Good"​

Early competition focused on "can it run the model"; now it's about "runtime efficiency, deployment cost":

Dimension	"Usable" era	"Good" era
Hardware performance	Can it run the model	Runtime efficiency, energy efficiency
Software stack	Basic adaptation	Maturity, framework breadth
Ecosystem	Existence	Developer community activity
Deployment cost	Insensitive	Core competitive factor

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V. H2 2026 Outlook​

5.1 Upcoming Key Events​

Time	Event	Impact
2026 Q3	NVIDIA Rubin architecture details revealed	Next-gen flagship specs unveiled
2026 Q3	Huawei Ascend 950PR/950DT formally launched	New benchmark for domestic inference chips
2026 Q4	AMD MI350X scaled delivery	NVIDIA Blackwell competitor
2026 Q4	Cambricon Siyuan 690 launch (est.)	New-gen training chip
2027 Q1	NVIDIA Blackwell Ultra delivery	New compute benchmark lands

5.2 Key Competitive Factors Over the Next Three Years​

1. Wafer capacity access: Advanced-process capacity is a scarce resource; vendors tied to SMIC and TSMC have inherent advantages
2. Capital operation efficiency: The IPO window is limited; raising enough capital on the market determines R&D sustainability
3. Software ecosystem depth: Hardware performance is only the entry ticket; software stack maturity, framework adaptation breadth, and developer community activity are the core moat

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VI. Conclusion: A Diverse Ecosystem Will Eventually Form​

In H1 2026, the AI chip industry is undergoing a historic transition from "one dominant player" to "pluralistic coexistence."

• NVIDIA builds higher training barriers with Blackwell Ultra while laying out inference efficiency via the Groq acquisition
• Huawei Ascend holds the domestic compute baseline with full-stack capability; 950PR begins to surpass H20 in inference
• Cambricon proves the commercial viability of domestic AI chips by turning profitable first; Siyuan 590 surpasses international rivals in specific scenarios
• Cerebras, Taalas and other inference-specific chip companies opened a "non-GPU" third route
• TSINGMICRO's reconfigurable architecture provides a diversified technical route choice for China's AI chips

Over the next three years, the domestic AI chip endgame will form a pluralistic ecosystem where GPU, ASIC, and reconfigurable computing three technical routes coexist, with cloud and edge developing in coordination. "Domestic substitution" is no longer a slogan, but an industrial reality happening now.

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Data sources:

• Xinchuang World "2026 China Domestic AI Chip Vendor Capability Quadrant" (2026-06-16)
• TrendForce "The Era of Inference Economy: The Rules of AI Chips Are Being Rewritten" (2026-06-04)
• RayByte "Compute Doubles! NVIDIA Blackwell Ultra Chip Launched" (2026-06-02)
• Official financial reports and announcements of each company

Related reading:

• Huawei Ascend 910C Specs Deep Dive
• Full Comparison Table

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