4 posts tagged with "AI-hardware"

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.

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.

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 2026, the AI hardware market is undergoing a fundamental shift from the "training race" to "deployment as king." As large models move from technology demos to large-scale commercial deployment, hardware form factors, technology roadmaps, and the competitive landscape are undergoing systematic change.

Publisher: CSHIA Research (中智盟咨询) Author: Zhou Jun

Five Core Trends​

Trend 1: Shift in compute demand structure — inference becomes the main engine of growth​

The biggest change in the 2026 AI hardware market is the shift in the center of gravity of compute demand from training to inference.

According to market data:

• In 2026, global AI inference compute demand is expected to grow over 60% year-over-year
• Inference compute will exceed training compute for the first time, becoming the dominant workload of AI infrastructure

This shift stems from AI applications moving from "model development" into the "large-scale deployment" stage — enterprises no longer train large models frequently, but instead transform AI capability into real business value through high-frequency inference calls.

Key manifestations​

1. Inference chip market explosion: Shipments of dedicated inference chips (ASICs) are expected to grow 129%, with their share of AI servers rising from under 20% in 2025 to 27.8%.

2. Cost structure optimization: NVIDIA's Rubin platform reduces inference token cost to 1/10 of the previous generation, pushing inference applications from "luxury" to "commodity."

3. Workload characteristics change: Inference tasks show "high-frequency, long-pipeline, low-latency" characteristics, demanding higher real-time responsiveness from hardware.

Latest GTC 2026 developments (June 1, Taipei)​

NVIDIA CEO Jensen Huang announced several major inference compute advances at GTC 2026 Taipei:

• Vera Rubin platform enters full production: The NVL72 rack system delivers agentic throughput 10× that of the previous-generation Grace Blackwell, designed for Agentic AI
• Vera CPU officially launched: 88-core Armv9.2 custom Olympus architecture, highest single-thread IPC in the world, 1.5TB LPDDR5X memory, 1.2 TB/s bandwidth, native FP8 support
• RTX Spark AI PC chip: Co-developed with MediaTek (codename N1X), Blackwell-architecture GPU with 1 PFLOP AI compute, 128GB unified memory, TSMC 3nm, reshaping the Windows PC ecosystem
• AI Factory platform DSX: Four components — DSX Sim (digital-twin simulation), DSX OS (resource orchestration), DSX MaxLPS (power optimization), DSX Flex (grid coordination)

This trend means the competitive focus for hardware vendors is no longer "peak single-card compute" but "inference energy efficiency" and "system-level optimization capability."

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Trend 2: Edge and on-device AI — the scaled deployment of compute moving downstream​

2026 is the pivotal year for edge AI hardware moving from proof-of-concept to scaled deployment.

As cloud inference cost pressure rises and privacy compliance requirements tighten, compute is accelerating its migration toward data sources, spawning explosive growth in hardware form factors such as edge servers, AI terminals, and smart devices.

Three deployment scenarios​

Scenario	Hardware form	Core characteristics	2026 market size forecast
Edge servers	Compact cabinets, edge compute nodes	Power density 40-80kW/cabinet, liquid cooling supported	Global shipments grow 28%
AI terminals	AI phones, AI PCs, smart glasses	On-device NPU compute 60+ TOPS, offline inference	1.5 billion units shipped
IoT devices	Smart cameras, sensors, robots	Low-power chips, real-time response	Market size exceeds $1.5 trillion

Technology breakthroughs​

1. On-device model compression: Through quantization, distillation and other techniques, models with tens of billions of parameters are compressed to run on-device.

2. Heterogeneous compute architecture: CPU+NPU+GPU coordination maximizes performance under power constraints.

3. Memory bandwidth optimization: Application of HBM technology in edge chips alleviates the "memory wall" problem.

The edge AI explosion means hardware design must balance "performance density" with "power efficiency," and traditional general-purpose chips face specialization challenges.

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Trend 3: Dedicated chips and heterogeneous computing — breaking the monopoly of a single architecture​

In 2026 the AI chip market will show a "one superpower, many strong players, a hundred flowers blooming" competitive landscape.

Although NVIDIA maintains its advantage in training, in segmented markets such as inference, edge, and specific scenarios, dedicated chips (ASICs) and heterogeneous computing solutions are rising rapidly.

Major technology roadmap comparison​

Chip type	Representative vendors	Core advantage	Applicable scenarios
General-purpose GPU	NVIDIA, AMD	Mature ecosystem, flexible programming	Cloud training, complex inference
Dedicated ASIC	Google TPU, Cambricon	High energy efficiency, cost advantage	Large-scale inference, specific algorithms
Compute-in-memory	Multiple startups	Breaks the "memory wall," low latency	Edge inference, real-time processing
FPGA/DPU	Xilinx, Huawei	Reconfigurable, high flexibility	Network acceleration, data preprocessing

Market landscape changes​

1. Domestic substitution accelerates: China's AI chip vendors raise their share in inference, edge and other scenarios to over 30%.

2. Open-source ecosystem rises: Open-source frameworks such as ROCm and OpenML lower the barrier to dedicated-chip development.

3. Chiplet technology popularizes: Integrating chips of different process nodes through advanced packaging achieves a balance of performance and cost.

4. GTC 2026 new products accelerate deployment (June 1, Taipei):

   • Vera Rubin platform: NVL72 rack system, agentic throughput 10× Grace Blackwell
   • Vera CPU: 88-core Olympus custom architecture, designed for Agentic AI low latency
   • RTX Spark: In partnership with MediaTek and Microsoft, reshaping the Windows PC ecosystem, 1 PFLOP AI compute
   • Nemotron 3 Ultra: SSM+MoE hybrid architecture, 5× faster inference, 30% lower cost

The core logic of this trend is: no single chip can dominate all AI scenarios; scenario fragmentation spawns technology-roadmap diversification.

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Trend 4: Energy efficiency and thermal management — from technical challenge to business bottleneck​

As AI chip power consumption breaks the kilowatt level (NVIDIA Rubin GPU reaches 2300W), energy efficiency and thermal management have been upgraded from "supporting technology" to "core bottleneck."

In 2026, single-cabinet power density will exceed 240kW, traditional air cooling completely fails, and liquid cooling changes from "optional" to "mandatory."

Key data​

• Power cost share: The share of power cost in AI data center operating cost rises from 15% to 35%
• Thermal value increases: A single GB300 server's liquid-cooling components are worth about $50,000, 15-20% of hardware cost
• PUE optimization: Liquid-cooled data centers can bring PUE down to under 1.1, but upfront investment rises 30%

Technology evolution directions​

1. Tiered liquid cooling: Cold-plate (mainstream), immersion (high density), two-phase cooling (frontier)

2. Power architecture upgrade: From 12V to 48V/800V high-voltage DC, reducing conversion losses

3. Intelligent thermal management: AI predictive cooling, dynamically adjusting cooling strategy based on load

This trend means a hardware vendor's competitiveness depends not only on chip performance but more on "system-level energy efficiency optimization capability"; the importance of supporting technologies such as thermal management, power delivery, and cabinet design rises substantially.

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Trend 5: AI-native hardware ecosystem — from "compatibility" to "reconstruction"​

In 2026, AI hardware is undergoing a paradigm shift from "adapting to AI" to "built for AI."

Traditional general-purpose hardware architectures struggle to meet the unique demands of AI workloads, spurring the rise of AI-native hardware design philosophy.

Three reconstruction directions​

1. Compute architecture reconstruction​

• Memory hierarchy optimization: HBM4 memory bandwidth breaks 3TB/s, compute-in-memory architecture reduces data movement
• Interconnect upgrade: NVLink 6.0 reaches 1.8TB/s bandwidth, supporting direct GPU-to-GPU communication
• Heterogeneous integration: Through advanced packaging, CPU, GPU and memory are stacked to boost bandwidth and reduce latency

2. Software-defined hardware​

• Reconfigurable logic: FPGA and DPU support dynamic algorithm loading, adapting to different AI models
• Compiler optimization: AI compilers (e.g., MLIR) automatically optimize hardware resource allocation
• Hardware abstraction layer: Unified programming interfaces shield underlying hardware differences

3. Ecosystem co-evolution​

• Model-hardware co-design: Large-model architectures account for hardware constraints (e.g., sparsification, quantization)
• Open-source hardware design: Application of RISC-V in AI chips lowers the development barrier
• Vertical integration: Cloud vendors' self-developed chips (e.g., AWS Graviton, Google TPU), software-hardware co-optimization

The essence of this trend is: the characteristics of AI workloads (matrix operations, high parallelism, memory sensitivity) are redefining hardware design principles, and the universality advantage of traditional x86 architecture is weakened in AI scenarios.

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Key Conclusions and Outlook​

1. The five trends are interconnected and mutually reinforcing​

The inference demand explosion drives edge deployment, edge scenarios spawn dedicated chips, high power consumption forces an energy-efficiency revolution, and all changes ultimately point to the reconstruction of the AI-native hardware ecosystem.

The core driver of this round of change is AI moving from "technology demo" to "commercial deployment"; hardware must satisfy the industry requirements of "scale, low cost, high reliability."

2. Opportunity windows for industry participants​

For industry participants, the opportunities in 2026 lie in:

• ✅ Capture the inference dividend: Deploy inference-specific chips and system optimization
• ✅ Deepen vertical scenarios: Customize hardware solutions for specific industries/applications
• ✅ Break the energy-efficiency bottleneck: Liquid cooling, high-voltage DC, AI thermal management and other technologies
• ✅ Build an open ecosystem: Open-source frameworks, open standards, cross-industry collaboration

Vendors that can provide "end-to-end solutions" rather than "single-point chips" will gain an advantageous position in this reshuffle.

3. Dynamic adjustment and continuous evolution​

The above analysis is based on early-2026 market data and industry forecasts; actual development may adjust dynamically due to factors such as technology breakthroughs, policy adjustments, and market demand changes.

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Industry Implications​

2026 is a watershed year for the AI hardware industry:

• From "compute race" to "deployment as king"
• From "single-point breakthroughs" to "system optimization"
• From "general-purpose architecture" to "dedicated customization"
• From "performance first" to "energy efficiency balance"

Vendors that can keenly capture trends, rapidly adjust strategy, and sustain technological innovation will seize the initiative in the AI hardware "era of deployment."

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References:

• CSHIA Research, "2026 AI Hardware: Five Transformations and the Rules for Survival"
• "AI Hardware Enters the 'Era of Deployment'," Sohu Tech, February 10, 2026