Intel and Tencent Cloud Release New-Generation Cloud Instances Based on Xeon 6+

Intel and Tencent Cloud Release New-Generation Cloud Instances Based on Xeon 6+; SmartChip Download Client Exclusive First Look November 03, 2026 19:10:21 From Beijing

SmartChip reported on November 3 that recently, at the 2026 Tencent Global Digital Ecosystem Summit, Intel and Tencent Cloud released Tencent Cloud's new-generation cloud instances based on Intel Xeon 6+ processors, and comprehensively showcased Intel's chip products and cloud practices.

The all-new Xeon 6+ processor features up to 288 efficiency cores, and a single processor can support the stable deployment of more than 1,000 agents, providing Tencent Cloud servers with cloud computing services featuring higher concurrency, lower latency, and better energy efficiency.

With the support of Xeon 6+, the comprehensive performance of Tencent Cloud's new-generation cloud instances is more than 30% higher than the previous generation, making them suitable for scenarios such as cloud-native microservices, high-concurrency Web, and agent task orchestration, further expanding Tencent Cloud's product capabilities for agentic AI workloads.

In addition, the new-generation cloud instances introduce Intel TDX (Trust Domain Extensions), which, through hardware-level isolation and attestation, build a full-lifecycle trusted defense line of "data usable but not visible" for agents.

For the next stage of higher-density task scheduling and concurrent computing, Intel will launch a Xeon processor (codenamed Diamond Rapids) designed specifically for next-generation agentic AI infrastructure, achieving a leap in throughput for high-load multitasking.

In response to the high concurrency, low latency, and other requirements that agentic AI places on infrastructure, Intel and Tencent Cloud also shared the latest practices on topics such as CPU performance, AI storage solution innovation, sandbox startup, and software ecosystem, jointly exploring the evolution path of cloud-based agentic AI infrastructure.

"Looking to the future, Intel looks forward to further deepening 'chip, cloud, and device' integrated collaboration with Tencent, jointly building a full-stack computing foundation for agentic AI, letting computing power go where it is needed, making intelligence within reach, and jointly driving agentic AI from technological innovation to large-scale application," said Guo Wei, Vice President of Intel Marketing Group and General Manager of Intel China.

▲Guo Wei, Vice President of Intel Marketing Group and General Manager of Intel China

1. Leveraging Intel IAA to Innovate Memory Solutions

For example, limited memory must carry more tasks, KV Cache must flow efficiently, be compressed, and be scheduled across different tiers, sandboxes must be started securely and quickly, and multimodal data must also be processed efficiently.

▲Intel Booth at Tencent Global Digital Ecosystem Summit

The deeper agentic AI goes into real business, the more precious memory becomes. How to simultaneously meet the dual requirements of density and latency in high-density, high-concurrency business scenarios is a problem that software solutions cannot solve, and it is also the starting point for Tencent Cloud and Intel in advancing memory application innovation.

According to the introduction, the Tencent team initially tested software solutions such as LZ4 and ZSTD. For LZ4, during single-channel decompression, the latency was still acceptable, but after multi-channel concurrency, the maximum latency once reached 5 milliseconds, failing to meet the requirements of this business scenario. In addition, the increase in concurrency also brought memory bandwidth contention.

With the help of Intel IAA (In-Memory Analytics Accelerator) built into Intel Xeon processors, Tencent Cloud innovated its memory solution, introducing hardware acceleration for memory compression and decompression to meet customers' requirements for extremely latency-sensitive decompression that must be strictly controllable.

IAA can provide hardware acceleration for compression and decompression, with advantages such as low latency, low power consumption, and low CPU usage. The Tencent team made lightweight adaptations to the kernel driver, simplified the relevant call paths, and tested different batch parallel configurations, ultimately choosing IAA-batch-8.

Compared with software solutions, after 2M-page decompression is empowered by IAA, decompression latency has been brought down to the microsecond level, with average time accelerated by 16 times and maximum time accelerated by 9 times. While improving memory utilization, it achieves more stable and predictable responses, so that memory no longer becomes the performance bottleneck of high-density, high-concurrency agentic AI. This solution has been verified by Tencent Cloud and has completed business deployment.

2. Building a Stable and Reliable Sandbox Foundation, with a Single Machine Supporting Concurrent Creation of 350 Sandboxes

For agentic AI to operate efficiently, the execution environment must also be available on demand. As agentic AI workloads begin to be deployed at scale, another important challenge facing infrastructure is how to create and start massive numbers of sandboxes in batches within an extremely short time, and efficiently complete the orchestration and isolation of execution environments.

Based on the deep collaboration between Intel Xeon processors and Tencent Cloud CubeSandbox, the two parties jointly built a sandbox foundation with high concurrency, extremely fast startup, and stability and reliability.

CubeSandbox is an elastic agent sandbox computing platform open-sourced by Tencent Cloud, carrying elastic workloads such as agent sandbox services and cloud functions. Relying on the computing performance, flexible scalability, and reliable platform capabilities of Xeon 6+ processors, CubeSandbox can efficiently support high-density sandbox workloads, achieving lower startup latency and higher resource utilization efficiency.

According to Tencent's actual measurements, while meeting the 200-millisecond SLA requirement, CubeSandbox equipped with Xeon 6+ processors can support 350 concurrent sandbox creation requests per machine, an improvement of about 46% over other mainstream test platforms.

Facing the demand for simultaneous startup of a large number of environments, Tencent Cloud has made considerable preparations at the software level. For example, some resources required to create sandboxes are pooled in advance, so that they can be directly used when requests arrive, reducing the overhead of temporary creation; memory and disk content that have not been modified are shared as much as possible to reduce duplicate occupation.

The Intel and Tencent teams also jointly investigated the kernel execution path and merged the relevant optimizations into the OpenCloudOS kernel to further reduce concurrent startup latency.

For agent services, such optimizations are of very practical significance. The faster the environment is prepared, the earlier tasks can begin; having the same machine carry more concurrent creation requests can further improve resource utilization efficiency; and remaining stable under high load can bring these capabilities closer to the requirements of production environments.

III. Fully leverage CPU resources to improve multimodal task efficiency

Execution environments need to be ready quickly, and agents entering real business scenarios also face another type of challenge: for example, rapidly processing embodied intelligence data in batches. The interaction between agents and the real world has long gone beyond text; video, images, audio and other data are entering workflows in large volumes, placing higher demands on data processing and inference efficiency.

In response to this change, Tencent Cloud's multimodal computing engine Xpark applies Intel AMX (Advanced Matrix Extensions), the built-in AI acceleration engine of Xeon processors, to use CPU resources for small-model inference and lightweight multimodal tasks, thereby improving AI computing throughput and overall resource utilization efficiency.

Xpark has several key capabilities, including a very rich set of high-performance multimodal operators, heterogeneous resource collaborative acceleration, Intel AMX instruction set acceleration, and distributed implementation of single-machine operators to achieve performance breakthroughs.

In the Xpark video processing workflow, with the support of iVTAL (Intel Video Transcode Acceleration Library), video transcoding performance is improved by 1.5 times compared with the open-source PyAV, and overall video processing efficiency is significantly improved.

At present, Xpark has been officially launched and is being applied in actual business scenarios.

Addressing storage requirements for large capacity, high performance, high energy efficiency and easy deployment, Intel solves issues such as the flow and reuse efficiency of KV Cache through high-speed I/O, built-in accelerators such as QAT and DSA, dedicated instructions such as UINTR+TPAUSE, and other software-hardware collaborative optimizations, enabling storage to move from fitting data to making it flow and achieving high efficiency.

IV. Building a system-level product portfolio to accelerate the large-scale deployment of agents

Intel has built a system-level solution for customers covering CPU, GPU, storage and networking, with a product portfolio spanning Xeon, Ethernet controllers and the GPU D series, among others.

Among them, the Intel GPU D series (codenamed Crescent Island) is purpose-built for continuously running, memory-intensive AI inference workloads: based on the Xe3P architecture, within a 350-watt power envelope, it can provide users with up to 480GB of LPDDR5X memory, 1.5TB/s memory bandwidth and 1.3 PFLOPS FP8 compute, making large-capacity inference easier to deploy.

To truly translate hardware performance into developer productivity, an open and easy-to-use software ecosystem is equally critical. Intel insists on open-sourcing its AI software stack and prioritizing contributions to upstream communities, covering mainstream frameworks such as PyTorch, vLLM and SGLang, as well as computing and communication libraries such as Triton, oneDNN and oneCCL, helping mainstream models achieve out-of-the-box usability.

In addition, for cross-platform migration of existing code, Intel has introduced an AI Agent based on large language models, combined with professional Skill capabilities, to natively refactor code on the basis of understanding algorithmic intent, and complete the full closed loop of compilation, verification and performance tuning, enabling cross-architecture migration to move from "running" to "running well."

At the same time, Intel extends computing power and agent capabilities from the cloud to terminals and the edge. End-side applications such as AI PCs equipped with WorkBuddy and Marvis and in-vehicle AI Boxes based on Intel Core Ultra processors were showcased together, vividly presenting the end-cloud collaborative end-side intelligent experience and bringing agentic AI into thousands of households.

▲Intel booth at the Tencent Global Digital Ecosystem Summit

Guo Wei, Vice President of Intel Marketing Group and General Manager of Intel China, said that from data centers to intelligent terminals, Intel and Tencent continue to expand the boundaries of cooperation, consolidating the cloud computing foundation with Xeon 6/6+, expanding end-side AI application space with agentic PCs, releasing full-stack intelligent potential through deep collaborative optimization of models and Agents, and relying on an open ecosystem to gather broader innovative forces to jointly connect the key links of agentic AI from infrastructure and models to applications.

From encountering bottlenecks in software testing, to introducing hardware acceleration, and then to driver adaptation and business launch, the case of cooperation between Intel and Tencent Cloud demonstrates a complete optimization path. Facing the trend of agents moving from technical exploration to all-scenario productivity, relying on system-level innovation covering computing power, storage power, software optimization and more, Intel is using Xeon as the computing hub and open, inclusive full-stack platform capabilities to build a high-performance intelligent computing foundation for the agentic AI era.

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