AI Accelerator PCIe Card Introduction

QY Research Inc. (Global Market Report Research Publisher) announces the release of 2025 latest report "AI Accelerator PCIe Card- Global Market Share and Ranking, Overall Sales and Demand Forecast 2026-2032". Based on current situation and impact historical analysis (2020-2024) and forecast calculations (2026-2032), this report provides a comprehensive analysis of the global AI Accelerator PCIe Card market, including market size, share, demand, industry development status, and forecasts for the next few years.

The global market for AI Accelerator PCIe Card was estimated to be worth US$ 4941 million in 2025 and is projected to reach US$ 13144 million, growing at a CAGR of 15.0% from 2026 to 2032.

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1. AI Accelerator PCIe Card Introduction
The PCIe Card serves as a critical interface for integrating advanced AI processing capabilities, acting as a bridge that enables seamless data transfer between the host system and the AI acceleration hardware. It leverages AI algorithms to optimize computational tasks, resulting in a significant enhancement of processing speed and efficiency. This card is engineered to support complex AI models, providing the necessary computational power to execute deep learning, machine learning, and neural network operations with precision and speed. By offloading intensive AI computations from the CPU, it ensures that the host system can maintain high performance across a range of applications, including data analytics, real-time decision-making, and advanced simulations.

Figure1: AI Accelerator PCIe Card Product Picture

AI Accelerator PCIe Card

Based on or includes research from QYResearch:

2. AI Accelerator PCIe Card Development Factors
2.1. High-Speed Interconnect Evolution: The Core Driver of AI Accelerator PCIe Card Performance Leap
The extreme demands of AI/ML workloads for bandwidth and latency dictate that PCIe cards must continuously evolve around being "faster, farther, and more stable." Their developmental factors are concentrated in three areas: protocol rate iteration, link form factor expansion, and the synergy of key auxiliary chips. Firstly, the accelerated generational succession of the PCIe protocol is significant, with link speeds rapidly transitioning from 32 GT/s of PCIe 5.0 to 64 GT/s of PCIe 6.0, and evolving towards 128 GT/s of PCIe 7.0. This provides a theoretical bandwidth that is multiplied for AI accelerator PCIe cards under a fixed number of lanes (x16), directly alleviating data "starvation" between the GPU/accelerator card and the CPU, memory, and other accelerators. Secondly, to adapt to ultra-large-scale computing clusters and heterogeneous computing architectures, the application scenarios of PCIe cards have extended from high-speed interconnect within a chassis to across motherboards and across racks. Solutions like Active Electrical Cables (AEC) and Active Optical Cables (AOC) break through the physical limitations of traditional copper traces, enabling accelerators to participate in more flexible system topologies using PCIe semantics. Thirdly, under the combined effect of doubled speeds and extended distances, signal integrity has become a key bottleneck constraining the stability of PCIe cards. Signal retiming and equalization chips like Retimers have therefore become "standard equipment" in AI servers. They reshape and compensate for high-speed signals between the PCIe card and the motherboard, backplane, and external links, ensuring controllable bit error rates and latency, even forming large-scale deployments within a single multi-GPU AI server. The superposition of these three aspects means that AI accelerator PCIe cards are no longer just an evolution in "interface form factor." Driven by the combined optimization of protocol, physical link, and system-level coordination, they have become a key foundational component supporting the continuous expansion of AI computing power.

2.2. Continuous Evolution of High-Speed Interconnects: The Key Driver for AI Accelerator PCIe Card Performance Advancement
The sustained explosive growth in performance of AI accelerator PCIe cards fundamentally relies on the rapid iteration and ecosystem maturation of the PCIe interconnect protocol. As the sole high-speed "data highway" connecting accelerator cards to the CPU, memory, and storage, the rate leaps of the PCIe standard directly determine the capability for massive data throughput and extremely low latency required by AI/ML workloads. Within just a few short years, from PCIe 4.0 (16 GT/s) to PCIe 5.0 (32 GT/s) and then to PCIe 6.0 (64 GT/s), bandwidth has achieved double or even quadruple improvements. The PCIe 7.0 (128 GT/s) specification is already in the development stage, providing ample channel capacity for next-generation inference and training of models with hundreds of billions of parameters. Concurrently, to break through single-chassis computing bottlenecks and construct ultra-large-scale AI clusters across servers and racks, PCIe link extension technologies have achieved major breakthroughs. Through PCIe Active Electrical Cables (AEC), Active Optical Cables (AOC), and Optical Retimers, the reliable transmission distance for PCIe signals has been extended from the traditional less than 1 meter to several meters or even tens of meters, supporting rack-level and even datacenter-level direct interconnect. The signal integrity challenges brought by high-speed, long-distance transmission have directly spurred explosive growth in the market for PCIe Retimer chips. Retimers effectively compensate for channel loss and jitter by receiving, performing Clock Data Recovery (CDR), equalizing, and re-driving high-speed differential signals, ensuring PCIe 5.0/6.0 reliability with BER < 10−12 over long distances. A typical 8-card AI server nowadays often requires the integration of 8-16 or even more Retimer chips. These factors work in concert, enabling AI accelerator PCIe cards to rapidly advance from tens of GB/s per card bandwidth to the hundreds of GB/s level. They have evolved from isolated deployment within a chassis to supporting new architectures for memory pooling, computing power sharing, and heterogeneous collaboration, such as CXL.cache, CXL.mem, and CXL.io. This continuously satisfies the "appetite" of generative AI, trillion-parameter model training, and real-time ultra-large-scale inference for extreme bandwidth and ultra-low latency, establishing them as the most critical infrastructure evolution path determining the performance ceiling of next-generation AI infrastructure.

2.3. From "Computing Power Dominance" to "Data Throughput Supremacy": The Structural Leap of AI Accelerator PCIe Cards in the AI Inference Era
In the AI inference era, influenced by characteristics such as small-batch requests, model fragmentation, and the widespread application of Mixture-of-Experts (MoE) architectures, the linear increase in single-card computing power can no longer directly translate into system-level performance gains. The frequency and real-time requirements for data exchange between multiple AI accelerators have significantly increased, causing communication latency and bandwidth to gradually replace raw computing capability as the core bottleneck constraining overall efficiency. The focus of system performance has clearly shifted from "computing power" to "data throughput" (i.e., data movement and interconnect capability). Against this backdrop, AI infrastructure architecture is transitioning from Scale-Out, which focuses on inter-rack connectivity, to Scale-Up, which centers on high-density interconnect within a rack, aiming to achieve higher bandwidth, lower latency, and more controllable deterministic performance by shortening physical distances. As the crucial high-speed interconnection hub between GPUs, and between GPUs and CPUs in this Scale-Up architecture, the AI Accelerator PCIe Card-especially those integrated with PCIe Switches-has seen its strategic value significantly amplified. On one hand, the continuous increase in per-lane bandwidth from newer PCIe generations (such as PCIe 5.0/6.0) enables the AI Accelerator PCIe Card to strike a balance between standardization, versatility, ecosystem maturity, and cost-effectiveness, making it a critical vehicle for inference-side deployment. On the other hand, as the scale of multiple cards within a single server expands, the PCIe card is no longer merely a "computing power carrier" but deeply participates in system-level topology construction and data scheduling. Its capabilities in low-latency forwarding, non-blocking interconnect, virtualization support, and multi-card collaboration efficiency directly determine the actual throughput performance and energy efficiency ratio of models like MoE. This, in turn, is driving rapid growth in demand for PCIe Switch chips and related PCIe accelerator cards. According to calculations by Soochow Securities, this sub-market is expected to reach a scale of hundreds of billions of RMB by 2027, reflecting the AI Accelerator PCIe Card's evolution from a "supporting hardware component" to a "core infrastructure" underpinning system efficiency in the inference era.

3. AI Accelerator PCIe Card Development Trends
3.1. Evolution of High-Bandwidth, Low-Latency Interconnect for Large-Scale Models
The development of AI Accelerator PCIe Cards will deeply revolve around the fundamental demands of AI model evolution. As AI models scale explosively, particularly driven by the extreme computational requirements of large-scale foundation models and inference/training tasks, next-generation PCIe standards such as PCIe 7.0 have been officially released. Its core objective, as explicitly stated by PCI-SIG in the official specification, is to meet the data transfer demands of AI data-intensive applications by significantly increasing bandwidth and efficiency, with PCIe 7.0 designed specifically for bandwidth-intensive scenarios like AI/machine learning, delivering higher transfer rates while maintaining backward compatibility. This enhancement in bandwidth and latency performance is foundational for supporting the training and inference of trillion-parameter-scale models, enabling GPUs and AI accelerator cards to collaborate more efficiently with system components such as CPUs and storage. Concurrently, innovations in AI architecture, like the Mixture of Experts (MoE) models, introduce new challenges for fine-grained and latency-sensitive communication. This demands that interconnects not only increase total bandwidth but also, at the protocol and topology levels, support more dynamic and irregular data exchange patterns. This necessity will drive continuous optimization of future PCIe and related interconnect technologies in terms of protocol efficiency and QoS mechanisms. Another driving factor is the widespread adoption of low-precision computations in AI inference and training, such as INT8 and INT4. This means a higher density of effective "computational data" needs to be transmitted within the same timeframe, placing greater demands on PCIe transmission efficiency and pushing AI accelerator internal architectures and data path designs to better align with low-precision computational characteristics. Furthermore, ecosystem collaboration and standard advancement are also shaping future trends; for instance, chip manufacturers are promoting tighter CPU-accelerator interconnect integration to enhance the performance and scalability of overall AI infrastructure. This AI model-driven evolutionary path will enable future AI Accelerator PCIe Cards to continuously evolve in aspects such as high bandwidth, low latency, intelligent scheduling, and dynamic communication support, thereby genuinely serving the needs of complex and massive AI computation.

3.2. Evolution of PCIe Accelerator Cards Towards Efficient Inference and Heterogeneous Collaboration Driven by Shifts in AI Computing Paradigms
With the maturation of AI technology and shifts in application demands, the focus of AI computing workloads is transitioning from "primarily emphasizing peak training compute power" towards a direction centered on "inference-dominated, cloud-edge collaborative computing." This trend is reshaping the design and functional positioning of AI Accelerator PCIe Cards. The inference phase has been recognized by industry leaders as the main arena for future AI computing. This is not only because inference computational volume is rapidly increasing within the overall AI ecosystem, but also because real-time performance, energy efficiency, and reducing operational costs have become core objectives in inference system design. NVIDIA's collaboration with Akamai to launch a globally deployed inference cloud stems precisely from the practical need in applications to push inference closer to edge nodes near users to reduce latency and improve experience. This strategy articulates the growing importance of inference compared to traditional training and emphasizes the necessity of achieving efficient inference across diverse deployment environments. This directly drives future PCIe accelerator card designs to place greater emphasis on performance-per-watt optimization, integrate specialized hardware units for efficiently handling specific inference tasks, and better support stages in end-to-end inference pipelines, such as video processing and secure computation. Simultaneously, the distributed trend of AI computing expanding from the cloud to the edge and endpoints necessitates the formation of a more efficient heterogeneous collaborative ecosystem within servers and across systems. Different types of acceleration units, such as CPUs, GPUs, ASICs, and NPUs, must rely on efficient system-level interconnects for scheduling and communication in practical deployments. PCIe, leveraging its universality and mature ecosystem advantages, continues to serve as the core "backbone" for connecting heterogeneous computing power, linking various computing elements and ensuring efficient data flow within the system to meet the demands of collaborative scheduling and low-latency communication. The future development of PCIe accelerator cards will not be limited solely to single-card performance. Instead, it will place greater emphasis on achieving unified scheduling and energy efficiency optimization within heterogeneous platforms, as well as supporting more flexible and efficient inference workloads across cloud-edge-end multi-level deployments. This ensures their ability to continuously meet the growing demands of real-time inference and distributed collaboration amid the shifting paradigms of AI computing.

3.3. The Evolution of Future PCIe Accelerator Cards Driven by AI System-Level Innovation: From Computing Unit to Resource Hub
As AI computing shifts from the pursuit of singular computing power towards system-level collaborative optimization, the role of PCIe-based accelerator cards within AI accelerators is undergoing a fundamental upgrade, becoming a critical hub for overcoming AI system bottlenecks. On one hand, traditional VRAM capacity struggles to meet the massive memory demands of large-scale models. The industry is addressing this by leveraging the open, high-speed interconnect standard Compute Express Link (CXL) to enable memory pooling and sharing across nodes. This technology allows CPUs and accelerators to access a unified memory address space, thereby overcoming the memory wall limitations of a single machine and significantly improving memory expansion efficiency through higher bandwidth and cache coherency. The CXL 4.0 specification is the latest embodiment of this trend, which substantially increases bandwidth and scalability over its predecessors to support large-scale AI system memory-sharing deployments, paving the way for multi-rack memory pools to become a reality. This dynamic scheduling capability for memory resources means that future CXL-enabled accelerator cards will not only provide computing power but also assume the role of memory expanders, endowing the entire AI system with a more flexible and composable architecture for computing and storage resources. Concurrently, to address the increasingly concentrated power consumption and thermal design challenges of AI clusters, data centers are increasingly adopting advanced liquid cooling designs to enhance energy efficiency and thermal dissipation density. Liquid-cooled accelerator cards and systems from industry leaders have become mainstream solutions. These solutions significantly improve thermal management capabilities through technologies like direct liquid cooling, establishing themselves as fundamental infrastructure supporting high-density AI deployments. Finally, at the level of the interconnect ecosystem, competition between open standards and closed ecosystems is influencing future development directions. Open interconnect ecosystems, such as CXL and the concurrently advancing open interconnect scheme UALink, enable more AI accelerator chip suppliers to gain fair access and collaboratively build a unified system resource pool. This reduces dependence on single-vendor proprietary interconnects and expands market choice. The strategic interplay of these interconnect ecosystems will continue to influence the path of system-level innovation and the market landscape in the coming years.

4. Leading Manufacturer in the Industry
4.1. Hitek Systems
Hitek Systems is a technology company specializing in the development of Field-Programmable Gate Array (FPGA) hardware, IP cores, and customized systems. Its primary business includes providing high-performance FPGA solutions, IP cores, development platforms, and engineering design services for the communications, data center, networking, and embedded markets. The company focuses on product and service development centered around the Intel Agilex series of FPGAs and related high-speed interconnect technologies. It emphasizes deep technical expertise in network protocol processing, high-speed Ethernet, computational storage integration, and customizable hardware acceleration, offering full-cycle support from conceptual design to hardware/software integration testing during hardware board development. Hitek Systems also develops a variety of FPGA system platforms and embedded module products, providing accompanying software support frameworks to shorten customers' design cycles. Its product portfolio covers application areas such as high-bandwidth communication, data acceleration, and system integration.

Hitek Systems' core product line is the HiPrAccTM series of FPGA accelerator cards. These accelerator cards are designed based on the Intel Agilex FPGA architecture and offer standard PCIe connectivity to support various computing and network acceleration tasks. Categorized by physical form factor, the company offers Half Height, Half Length cards such as the HiPrAccTM NC100, NC220, and C220. These cards provide compact-form-factor network and computing acceleration capabilities for data center and edge computing scenarios. Concurrently, Hitek Systems' product line includes Full Height, Full Length cards, such as the NCS280-I, NCS200, and CS200D. These support composite workloads for networking, computing, and storage acceleration through larger board sizes and richer interfaces. Among them, the CS200D is a dual-FPGA computational storage module suitable for high-performance computing environments. All the aforementioned PCIe accelerator cards comply with the PCIe interface specifications defined by PCI-SIG. Through standardized host connectivity combined with an open FPGA software stack, they can be integrated into existing servers and acceleration platforms to support workloads such as machine learning inference, network processing, and high-bandwidth data streaming.

4.1.1. Key Features of HiprAccTM NCS280-I
HiprAccTM NCS280-I is an AI Accelerator PCIe Card designed for high-performance computing and AI acceleration in data centers. It is based on the Intel Agilex 7 I-Series FPGA (supporting AGI023/AGI019 devices), utilizes the F-Tile architecture, and enables high-speed host connectivity via PCIe Gen5 x16 (512Gbps) and CXL (Compute Express Link) interfaces. It is compatible with a low-profile, half-length, full-height, single-slot form factor (dimensions 6.6"×4.376"), with a maximum power consumption of 75W (edge-powered) or 100W (with an additional 6-pin PCIe power supply). The card is equipped with up to 48GB of DDR4 memory (2×72-bit channels, supporting 8GB/16GB configurations), supports up to 4 Gen4 M.2 NVMe SSDs for expansion, and integrates Agilex ARM HPS (quad-core Cortex-A53) + 32GB eMMC and a GigE debug interface. Through dual QSFP28 ports, it delivers high-speed networking capabilities of up to 2×200Gbps/100Gbps or 8×50G/25G/10G. The F-Tile supports Ethernet speeds up to 400Gbps (8×56Gbps PAM-4). The card supports the oneAPI high-level abstraction development workflow and is suitable for machine learning inference/training, network acceleration, storage offload, 5G infrastructure (DU/CU), and large-scale AI workloads in data centers. It offers robust programmability, high-bandwidth I/O, and flexible clock synchronization (PTP/1588), serving as a domestic alternative solution for high-performance AI and network convergence acceleration.

4.2. NVIDIA
NVIDIA's business is centered on "accelerated computing," delivering computing products and platform software across data centers and cloud computing, gaming and content creation, professional visualization and digital twins, as well as automotive and robotics, all built around the GPU as the core hardware foundation. On the data center side, NVIDIA integrates GPU computing, CPUs, DPUs, and end-to-end networking into unified compute and networking platforms designed for AI training and inference, and encapsulates hardware capabilities through CUDA and related developer software stacks so they can be directly leveraged by mainstream frameworks and applications, thereby enabling the large-scale deployment of generative AI, data analytics, and high-performance computing workloads in enterprise and cloud environments. In graphics and visualization, NVIDIA provides GPUs and software ecosystems for gaming, content creation, professional graphics, and simulation, emphasizing the reuse and synergy of a unified architecture and software platform across different markets, and using a single core computing architecture and development toolchain to support deployments from the cloud to the edge. NVIDIA's AI Accelerator PCIe Card business is a key component of its data center segment, with the primary objective of deploying GPU acceleration at scale in standard PCIe form factors within general-purpose servers to serve cloud service providers, enterprise data centers, and edge computing environments. This business is built around NVIDIA's data center GPU product lines and achieves compatibility with mainstream CPU platforms and server architectures through PCIe interfaces, enabling AI training, inference, data analytics, and high-performance computing to be rapidly deployed within existing IT infrastructures. In terms of product positioning, NVIDIA offers PCIe GPUs across different power envelopes and board form factors to address workloads ranging from lightweight inference to high-intensity inference and training, and emphasizes consistent acceleration experiences across public cloud services, private enterprise data centers, and industry applications through a unified CUDA software platform, driver stack, and AI framework support. Official disclosures indicate that within its AI Accelerator PCIe Card portfolio, NVIDIA provides half-height half-length PCIe cards for high-density deployments and inference scenarios, as well as data center-class PCIe card products designed for higher compute requirements, and delivers these PCIe accelerators in coordination with its data center networking, systems, and software stacks as complete accelerated computing platforms, forming a core delivery model of its data center AI business.

4.2.1. Key Features of L4 Tensor Core GPU
The L4 Tensor Core GPU is an AI Accelerator PCIe Card specifically designed for efficient AI inference, video processing, and graphics acceleration in data centers. It is based on the Ada Lovelace architecture and manufactured using a 4nm process. It can be conveniently installed into standard servers via a high-speed PCIe Gen4 x16 interface (bandwidth 64GB/s, backward compatible with Gen3), with a maximum Thermal Design Power (TDP) of only 72W. It supports passive cooling and features a single-slot, half-height, half-length, low-profile form factor (HHHL-SS, dimensions approximately 169mm × 69mm), offering extreme energy efficiency and suitability for high-density edge and cloud deployments. The card is equipped with 24GB of GDDR6 memory (with ECC support), a 192-bit memory interface, and a bandwidth of 300GB/s. It integrates fourth-generation Tensor Cores, supporting various precision computations including FP32 (30.3 TFLOPS), TF32 (120 TFLOPS), FP16/BF16 (242 TFLOPS), FP8 (485 TFLOPS), and INT8 (485 TOPS) (*peak values with sparsity acceleration), with INT8/FP8 inference performance significantly improved compared to the previous generation. It features 2 NVENC encoders, 4 NVDEC decoders, and 4 JPEG decoders, supports the AV1 format, and enables ultra-high-concurrency video stream processing (e.g., over 1000 streams of 720p30 AV1 encoding in an 8-card server). It is suitable for scenarios such as large-scale generative AI inference, recommendation systems, visual AI, natural language processing, real-time video transcoding/analysis, virtual desktops (vGPU/vWS/vPC), Omniverse real-time rendering, and cloud gaming. Compared to CPU-based solutions, it offers up to 120x improvement in AI video performance, 2.7x improvement in generative AI, and over 4x improvement in graphics rendering. It provides a domestic alternative-level AI infrastructure solution with leading energy efficiency and versatile acceleration capabilities.

4.3. Beijing Cambricon Technologies Corporation
Cambricon is a Chinese technology company focused on artificial intelligence computing chips and acceleration hardware, centered around its proprietary AI processor architecture, forming a comprehensive AI computing product line covering cloud, edge, and end-devices. The company's core business revolves around AI processor chip design, intelligent accelerator card and system hardware development, and the corresponding software ecosystem. Through its self-developed processor architecture and comprehensive software platform, it serves data center, cloud computing, intelligent device, and industrial intelligent transformation scenarios, supporting various computing needs from AI inference to training. Cambricon's product portfolio includes data center accelerator cards for high-performance AI computing, dedicated chips for edge and end-devices, and the supporting software stack (e.g., NeuWare, MagicMind), leveraging a device-cloud synergy technological strategy to help customers deploy and apply intelligent computing power.

Regarding AI Accelerator PCIe Cards, Cambricon offers a variety of PCIe accelerator card products based on its proprietary AI chips, categorized into different types according to physical specifications and application positioning. Under the Cambricon "Siyuan 370" series, accelerator cards include the half-height, half-length MLU370-S4/S8 intelligent accelerator cards, which are compact and suitable for high-density deployment, primarily targeting cloud inference workloads. The full-height, full-length MLU370-X4 intelligent accelerator card offers greater computing power and memory capacity, making it more suitable for data center scenarios requiring higher inference and training performance. The larger-sized MLU370-X8 intelligent accelerator card adopts a dual-chip design and supports multi-card interconnection, targeting more high-end AI training tasks. Cambricon also provides accelerator cards based on the earlier "Siyuan 270" series, such as the MLU270-S4 (half-height, half-length) and MLU270-F4 (full-height, full-length), which are deployed in servers via standard PCIe interfaces for general-purpose AI inference and computing acceleration. All the aforementioned accelerator card types interconnect with the host system through standard PCIe interfaces. Combined with Cambricon's foundational software platform, they support the execution of diverse AI workloads, including vision, speech, and natural language processing.

4.3.1. Key Features of MLU270-S4
Cambricon's MLU270-S4 (Siyuan 270-S4) is a data center-class AI Accelerator PCIe Card designed specifically for high-efficiency AI inference, adopting Cambricon's second-generation MLUv02 architecture chip "Siyuan 270" manufactured on a 16nm process. It is rapidly deployed in servers via the ×16 PCIe Gen.3 interface, with a maximum power consumption of only 70W, supporting passive cooling. The form factor is single-slot, half-height, half-length (dimensions 167.5mm × 68.9mm, weight approximately 310g), making it highly suitable for high-density, low-power data center environments. The card is equipped with 16GB of DDR4 ECC memory, featuring a 256-bit memory interface width and a bandwidth of up to 102 GB/s. It supports various low-precision and mixed-precision computations including INT16, INT8, INT4, FP32, and FP16, with theoretical peak performance of 128 TOPS for INT8, 256 TOPS for INT4, and 64 TOPS for INT16. Compared to the previous generation Siyuan 100, the theoretical peak performance for processing non-sparse AI models is improved by 4 times. It can be widely applied to diverse AI inference scenarios such as vision, speech, natural language processing, and traditional machine learning, helping to build ultra-high-efficiency AI inference platforms.

4.4. Kunlunxin (Beijing) Technology
Kunlunxin is a technology enterprise dedicated to artificial intelligence computing chips and acceleration hardware. Its technological foundation originates from Baidu's long-term research and development practices in the AI acceleration field, and it has achieved market-oriented development through independent operations. The company focuses on chip and overall system design around its self-developed AI processor architecture, creating a general-purpose AI chip product line that covers both edge inference and data center computing scenarios, and building a corresponding software ecosystem to support AI application deployment by developers and industry customers. Kunlunxin emphasizes the versatility and ease-of-use of its self-developed XPU architecture, enabling its chips to demonstrate strong adaptability in various AI tasks such as natural language processing, computer vision, speech, and traditional machine learning. It simultaneously advances the coordinated development of edge and cloud AI computing capabilities. Its products have already been deployed and utilized in real-world scenarios such as the internet, smart campuses, and smart transportation, and play a role in promoting the development of large models and AI infrastructure.

Kunlunxin has launched multiple standard PCIe accelerator card products based on its self-developed AI chips. These accelerator cards are categorized into form factors such as half-height, half-length, and full-height, full-length to adapt to different server chassis and deployment environments. The company's products include the half-height, half-length Kunlunxin AI Accelerator Card K100. This model employs its first-generation AI chip design, is intended for edge inference scenarios, and features low power consumption and a small form factor. Also based on the second-generation AI chip, the Kunlunxin AI Accelerator Card R100 similarly adopts a half-height, half-length form factor, targeting edge inference and lightweight AI tasks to enhance AI computing efficiency for small-scale deployments. Full-height, full-length accelerator cards are primarily used for data centers or higher-density AI computing workloads. For example, the Kunlunxin AI Accelerator Card R200 series requires a standard PCIe4.0 x16 slot and additional power supply to support high-performance inference tasks. This category also includes the Kunlunxin AI Accelerator Card RG800, positioned for data center training and concurrent multi-service inference. Kunlunxin also provides an integrated solution with the AI Accelerator Cluster R480-X8, which achieves large-scale parallel computing by integrating multiple acceleration units on a unified substrate, suitable for the training and inference deployment of large models. All the aforementioned accelerator card products interconnect with the server host via standard PCIe interfaces and, combined with Kunlunxin's software stack, support various AI frameworks and scenarios.

4.4.1. Key Features of R200 Series
The R200 series is an AI Accelerator PCIe Card designed for high-performance AI inference in data centers. It employs the Kunlunxin second-generation self-developed XPU-R architecture chip, manufactured using an advanced 7nm process. It is conveniently installed into standard servers via a high-speed PCIe Gen4 x16 interface (backward compatible with 3.0/2.0/1.0), with a typical power consumption of 150W and supports passive cooling design, making it suitable for high-density deployment. This series is equipped with 16GB or 32GB of high-speed GDDR6 memory, delivering memory bandwidth up to 512 GB/s. It supports various precision computations including INT8, INT16, FP16, and FP32, with a peak performance of 256 TOPS for INT8 and 128 TFLOPS for FP16, combining powerful versatility with high energy efficiency. It features a built-in hardware video encoding/decoding unit, supporting a maximum decoding capability of up to 108 channels of 1080P@30FPS. It is comprehensively adapted for multi-scenario AI inference tasks such as natural language processing, computer vision, speech recognition, traditional machine learning, and video analytics. It is particularly suitable for high-throughput, low-latency applications including large model inference, internet services, and intelligent finance. It provides a domestic AI acceleration solution with performance close to or exceeding mainstream GPUs and superior cost-effectiveness.

The report provides a detailed analysis of the market size, growth potential, and key trends for each segment. Through detailed analysis, industry players can identify profit opportunities, develop strategies for specific customer segments, and allocate resources effectively.

The AI Accelerator PCIe Card market is segmented as below:
By Company
Hitek Systems
NVIDIA
Axelera
ASUS
Hailo
EdgeCortix
Inspur Electronic Information Industry
Huawei
Beijing Cambricon Technologies Corporation
Jiangyuanxin Technology (Shanghai)
Shanghai Enflame Technology
Kunlunxin (Beijing) Technology
Shanghai Biren Intelligent Technology
Shanghai Tianshu Zhixin Semiconductor
Beijing Moore Threads Technology

Segment by Type
Single PCIe Card
Dual PCIe Card

Segment by Application
Cloud Servers
Data Centers
Other infrastructure

Each chapter of the report provides detailed information for readers to further understand the AI Accelerator PCIe Card market:

Chapter 1: Introduces the report scope of the AI Accelerator PCIe Card report, global total market size (valve, volume and price). This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry. (2021-2032)
Chapter 2: Detailed analysis of AI Accelerator PCIe Card manufacturers competitive landscape, price, sales and revenue market share, latest development plan, merger, and acquisition information, etc. (2021-2026)
Chapter 3: Provides the analysis of various AI Accelerator PCIe Card market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments. (2021-2032)
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.(2021-2032)
Chapter 5: Sales, revenue of AI Accelerator PCIe Card in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world..(2021-2032)
Chapter 6: Sales, revenue of AI Accelerator PCIe Card in country level. It provides sigmate data by Type, and by Application for each country/region.(2021-2032)
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc. (2021-2026)
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.

Benefits of purchasing QYResearch report:

Competitive Analysis: QYResearch provides in-depth AI Accelerator PCIe Card competitive analysis, including information on key company profiles, new entrants, acquisitions, mergers, large market shear, opportunities, and challenges. These analyses provide clients with a comprehensive understanding of market conditions and competitive dynamics, enabling them to develop effective market strategies and maintain their competitive edge.

Industry Analysis: QYResearch provides AI Accelerator PCIe Card comprehensive industry data and trend analysis, including raw material analysis, market application analysis, product type analysis, market demand analysis, market supply analysis, downstream market analysis, and supply chain analysis.

and trend analysis. These analyses help clients understand the direction of industry development and make informed business decisions.

Market Size: QYResearch provides AI Accelerator PCIe Card market size analysis, including capacity, production, sales, production value, price, cost, and profit analysis. This data helps clients understand market size and development potential, and is an important reference for business development.

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