PyTorch Conference China 2026：阿里、蚂蚁

PyTorch Conference China 2026 brought the PyTorch community together in Shanghai on September 8–9 alongside KubeCon + CloudNativeCon and OpenInfra Summit, following sponsor-hosted co-located events on September 7. Technical discussions spanned models, frameworks, distributed training, inference, hardware, cloud native infrastructure, and agents.

“Open Source for the AI Era” framed work across those layers. Across co-located sessions, keynotes, technical demonstrations, a PyTorch Foundation press conference, community meetings, and conversations at the PyTorch booth, the program covered hardware adaptation, training and serving, open infrastructure, accelerator integration, and collaboration across open source communities.

PyTorch Foundation welcomed Alibaba Cloud, Ant Group, and Cambricon as new members, joining Huawei and other existing Foundation members.

Building Frontier Intelligence in the Open

Speaker: Mark Collier, Executive Director, PyTorch Foundation

Mark’s September 8 keynote focused on an open ecosystem where researchers, developers, model builders, and hardware companies can work together across an expanding landscape of models and compute. The session connected PyTorch with the path from experimentation to production, new accelerator architectures, and the infrastructure that ultimately runs AI workloads.

Additional Keynotes Across the Open Source AI Stack

The keynote program brought together speakers working across models, PyTorch, cloud native infrastructure, accelerators, serving, and agents.

Day 1

From Open Source Adoption to Open Source Innovation: China’s Next Chapter

Speaker: Professor Lu Shouqun

Organization: China OSS

Open Source for the AI Era

Speakers: Jonathan Bryce and Horace Li

Organization: The Linux Foundation

Beyond Multimodal: Building Full-Modal AGI via Open-Weight

Speaker: Ryan Lee

Organization: MiniMax

Operating Frontier Intelligence at Scale

Speakers: Chris Aniszczyk and Xiao Zhang

Organizations: The Linux Foundation and Dynamia.ai

Tidal Auto-scaling for Training and Inference Based on Kubernetes + KEDA

Speakers: Jun Zheng and Tan Pei Xiang

Organization: China Merchants Bank

Ascend & PyTorch: Pioneer New AI Open Ecosystem

Speaker: Liang Zhang

Organization: Huawei

Towards Device-agnostic PyTorch: Building Unified Infrastructure for a Multi-Backend Ecosystem

Speaker: Wei Li

Organization: Cambricon

Inside vLLM: Production Best Practices, Model Integration and Road Map

Speaker: Kaichao You

Organization: Inferact Inc.

PD Disaggregation vLLM Deployment on Alternative AI Accelerators Using llm-d

Speakers: 纪飞 王 and Mengxuan Li

Organizations: Dynamia and Dynamia.ai

Building an Agent Runtime with Open Infrastructure

Speakers: Yaya Xia and Xu Wang

Organization: Ant Group

What AI Agents Need from Open Infrastructure

Speaker: Yaya Xia

Organization: Ant Group

Day 2

Welcome Back + Opening Remarks

Speakers: Jonathan Bryce and Horace Li

Organization: The Linux Foundation

What Powers Frontier Intelligence

Speaker: Thierry Carrez

Organization: OpenInfra Foundation

Road from Kata Containers to Confidential Containers + GPUs: From First Commit to CNCF Incubation

Speaker: Zvonko Kaiser

Organization: NVIDIA

HyperParallel: A SuperPoD-Aware Distributed Acceleration Library

Speakers: Teng Su and Shendi Wang

Organization: Huawei

Build a Unified Heterogeneous AI Computing Ecosystem for PyTorch

Speaker: Zesheng Zong

Organization: Huawei

Serving Qwen at Scale: Multi-Cluster AI Infrastructure on Karmada

Speaker: Jionghang Cai

Organization: Alibaba Cloud

Meet the Community Behind the Open Source AI Stack

Speakers: Jane Lyu; Fupan Li; Zesheng Zong; Hiu Yeung, Sunny Chan

Organizations: The Linux Foundation; Ant Group; Huawei; TCC Consulting Limited

Building Frontier AI Infra: SGLang and Miles

Speaker: Ke Bao

Organization: RadixArk

A Cloud Native Stack from Bare Metal to Tokens for Large-Scale AI Inference

Speaker: Trong Vinh Nguyen

Organization: Viettel

Alibaba Cloud, Ant Group, and Cambricon Join PyTorch Foundation

PyTorch Foundation announced three new members in Shanghai on September 8.

Alibaba Cloud joined as a Platinum Member, with work spanning cloud infrastructure and the Qwen model family.

Ant Group joined as a Gold Member, bringing experience with production AI at financial-services scale.

Cambricon joined as a Platinum Member. Cambricon develops MLU accelerators and follows an “Upstream First” approach to its PyTorch contributions. Its contributions span torch.compile, Eager Operators, Device Runtime, Distributed Computing, AMP, Dataloader, and Profiler.

Representatives from Alibaba Cloud, Ant Group, Cambricon, and Huawei took the keynote stage to speak about the open AI stack across hardware, models, and infrastructure.

ANY MODEL · ANY CHIP · ANY CLOUD · ANY AGENT

Read the full membership announcement.

Read more about Cambricon joining PyTorch Foundation as a Platinum Member.

One Open Source AI Stack

At the September 8 press conference, PyTorch Foundation outlined an open source AI stack spanning applications and agents, building and delivering intelligence, running and scaling AI workloads, infrastructure and isolation, and heterogeneous compute.

PyTorch, vLLM, and Ray represented the layer focused on building and delivering intelligence. Kubernetes, KServe, Kueue, OpenTelemetry, and llm-d represented the layer focused on running and scaling AI workloads. OpenStack and Kata Containers represented infrastructure and isolation. Those software layers run across NPUs, CPUs, GPUs, and other accelerators.

PyTorch Foundation members work across four areas:

• Any Model: AI labs and builders
• Any Chip: silicon and systems
• Any Cloud: clouds and platforms
• Any Agent: production and services

Many members work across more than one layer. The Foundation summarized the model directly: “No single organization builds the whole system. Together, our members span it.”

China in the Stack

100+ China-based developers contribute to PyTorch across 40+ affiliated organizations.

PyTorch Foundation members represented in the China-focused overview included Alibaba Cloud, Cambricon, and Huawei as Platinum Members, Ant Group as a Gold Member, and Beijing Academy of Artificial Intelligence (BAAI) as an Associate Member.

The September 8 membership announcement also noted that more than 250 organizations across China contribute to PyTorch Foundation projects, including DeepSpeed, Helion, PyTorch, Ray, Safetensors, and vLLM.

The conference also highlighted open model development and optimization through the open software layer.

For DeepSeek-R1, one case study covered optimization through kernels, routing, parallelism, and serving on the same GB300 hardware six months later. In the configuration presented, the optimized system delivered 2.77x throughput and 60% lower token cost. The source cited in the conference material was NVIDIA, 2026.

A second example showed the share of OpenRouter token traffic served by open-weight models developed in China increasing from 2% in late 2024 to 45% in April 2026. The source cited in the conference material was Mozilla.

Join the PyTorch TAC Accelerator Integration Working Group

AI computing hardware faces heterogeneity challenges with high adaptation costs and a lack of unified standards.

The PyTorch TAC Accelerator Integration Working Group, co-chaired by Huawei and Intel, delivers standardized hardware onboarding guidelines, a cross-repo CI testing mechanism, generalized device-aware test suites, and platform incubation workflows.

Zesheng Zong shared the Accelerator Integration Working Group’s achievements and future roadmap during PyTorch Conference China, including refined device-agnostic APIs and an expanded multi-backend test matrix.