Conferences / PyTorch Conference
PyTorch Conference Europe 2026
Apr 7–8, 2026 · Station F, Paris, France
- Conference
- PyTorch Conference Data science & ML
- Dates
- Apr 7, 2026 – Apr 8, 2026
- Venue
- Station F
- Place
- Paris, France
- Format
- In person
- Status
- Past
- Attendance
- 600PyTorch blog recap: 'brought together more than 600 researchers, developers, practitioners, and academics' (600+).
- Website
- events.linuxfoundation.org
- Recorded
- 13 sponsors · 204 speaker appearances
- Notes
- First PyTorch Conference Europe, hosted by the PyTorch Foundation. Full Sched programme recorded (120 sessions, breaks excluded); affiliations as shown on Sched.
Ticket prices
| Ticket | Price | Valid until | Notes | Source |
|---|---|---|---|---|
| Attendee (super early bird) | €299 | Jan 30, 2026 | events.linuxfoundation.org | |
| Attendee (early bird) | €449 | Feb 27, 2026 | events.linuxfoundation.org | |
| Attendee (standard) | €549 | Mar 20, 2026 | events.linuxfoundation.org | |
| Attendee (late) | €649 | Apr 8, 2026 | events.linuxfoundation.org | |
| Academic | €199 | — | Same price in all periods; proof of academic ID required. | events.linuxfoundation.org |
Sponsors 13
Title / headline 4
- AMD · Diamond
- Cloud Native Computing Foundation · Diamond
- Meta · Diamond
- Red Hat · Diamond
Silver 3
Bronze 2
Startup 1
- Yasp.ai · Startup + Non-Profit
Speakers and talks 204
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Artur Niederfahrenhorst | Anyscale | Keynote | Keynote: vLLM & Ray Updates - Tyler Michael Smith, Chief Architect | — |
| Besmira Nushi | NVIDIA | Keynote | Keynote: The Unbearable Lightness of (Agentic) Evaluations | — |
| Edward Yang | Meta | Keynote | Keynote: PyTorch Updates | — |
| Joe Spisak | Reflection AI | Keynote | Keynote: Community Led Open Source RL | — |
| Jonathan Bryce | The Linux Foundation | Keynote | Sponsored Keynote: Open Source Infrastructure for the AI Native Era | — |
| Leonard Hussenot | Keynote | Keynote: Gemma 4: Compacting Intelligence for the Edge | — | |
| Lysandre Debut | Hugging Face | Keynote | Keynote: The Hub as Infrastructure. From Open PyTorch Models, to a Safe and Performant Distribution Hub | — |
| Mark Collier | The Linux Foundation | Keynote | Keynote: Co-Evolution: How the Open Source Intelligence Stack Compounds | — |
| Maryam Tahhan | Red Hat | Keynote | Sponsored Keynote: Any [ Agent | Model | Accelerator | Cloud ]. Open Source AI Unlocks the World's Potential | — |
| Matt White | The Linux Foundation | Keynote | Keynote: PyTorch CTO | — |
| Nicolò Lucchesi | Red Hat | Keynote | Sponsored Keynote: Any [ Agent | Model | Accelerator | Cloud ]. Open Source AI Unlocks the World's Potential | — |
| Patrick von Platen | Mistral | Keynote | Keynote: Stream Everything - Moving from Request input to Streaming input | — |
| Ramine Roane | AMD | Keynote | Sponsored Keynote: From One Node to Distributed Training and Inference. How the PyTorch Ecosystem Changed AI | — |
| Tyler Michael Smith | Red Hat | Keynote | Keynote: vLLM & Ray Updates - Tyler Michael Smith, Chief Architect | — |
| Aamir Nazir | Intel | Poster | Poster Presentations: Inference & Production | — |
| Abhinav Srivastav | TraceOpt | Lightning talk | Lightning Talk: Step-Aligned Telemetry for Distributed PyTorch Training (Time & Memory Attribution Across Ranks) | — |
| Akash Agrawal | Fujitsu | Poster | Poster Presentations: Frameworks & Compilers | — |
| Aki Makkonen | Alif Semiconductor | Lightning talk | Lightning Talk: Running ExecuTorch Applications With Silicon Acceleration, in Ultra-low Power | — |
| Alban Desmaison | Meta | Speaker | Meet the Developers of PyTorch Module Maintainers | — |
| Allen Wang | Meta | Speaker | TorchStore: What We Learned Building Distributed Storage Solutions for AysncRL | — |
| Alpha Romer Coma | Kollab Philippines | Speaker | De-mystifying PyTorch for ASICs: When (and Why) To Move Your Development To AI Accelerators | — |
| Amir Afzali | Meta | Speaker | TorchStore: What We Learned Building Distributed Storage Solutions for AysncRL | — |
| Andrea Mattia Garavagno | University of Genoa & Scuola Superiore Sant'Anna | Speaker | Build PyTorch to Understand PyTorch | — |
| Andrew Wang | Blur Labs | Lightning talk | Lightning Talk: How DeepInverse Is Solving Imaging in Science and Healthcare With PyTorch | — |
| Andrii Krutsylo | Polish Academy of Sciences | Lightning talk | Lightning Talk: Backpropagation-Free Optimization in PyTorch | — |
| Angela Yi | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Anil Vishnoi | Red Hat | Poster | Poster Presentations: Inference & Production | — |
| Ankita Luthra | Lightning talk | Lightning Talk: Bringing Google’s Colossus to PyTorch: Rapid Storage via fsspec to Keep GPUs Busy | — | |
| Anshul Sinha | Meta | Lightning talk | Lightning Talk: Implementing Single-Dim Strategies With Sharding Validator | — |
| Artur Niederfahrenhorst | Anyscale | Speaker | Meet the Ray Maintainers | — |
| Ashesh Vidyut | Speaker | Beyond JSON-RPC: Scaling Model Context Protocols With gRPC in the PyTorch Ecosystem | — | |
| Asma Farjallah | NVIDIA | Speaker | Birds of A Feather: NCCL in the Wild: Scaling Communications To Thousands of GPUs | — |
| Bakhtiyar Mammadli | National Centre for Nuclear Research | Poster | Poster Presentations: Applications & Case Studies | — |
| Ben Burtenshaw | Hugging Face | Speaker | Bridging the Hardware Gap With Code Harnesses on the Hugging Face Kernels Hub | — |
| Billy McFall | Red Hat | Lightning talk | Lightning Talk: Slash LLM Cold-Start Times by Pre-distributing GPU Caches | — |
| Bin Bao | Meta | Lightning talk | Lightning Talk: Accelerating PyTorch Models With Torch.compile's C++ Wrapper Mode | — |
| Burkhard Ringlein | IBM | Speaker | Portable High‑Performance LLM Serving: A Triton Backend for VLLM | — |
| Carol Chen | Red Hat | Speaker | Teaching PyTorch To Read Your Worst PDFs With Docling | — |
| Christin Pohl | Microsoft | Speaker | Tour De Force: LLM Inference Optimization From Simple To Sophisticated | — |
| Claudio Basile | Speaker | Sponsored Session: TorchTPU: Expanding TPU Programmability to Pytorch | — | |
| Clodagh Walsh | Red Hat | Speaker | From Gradients To Governance: Making PyTorch Lineage-Aware | — |
| Cormac Brick | Lightning talk | Lightning Talk: From Hugging Face To Handheld: Scaling LLM Deployment With LiteRT Generative API | — | |
| Crefeda Rodrigues | Arm | Speaker | Optimizing CPU LLM Inference in PyTorch: Lessons From VLLM | — |
| Cyril Kondratenko | Nebius | Speaker | Sponsored Session: Fault-Tolerant Training: How We Build Reliable Clusters for Distributed AI Workloads | — |
| Daniel Flores | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Daniel Galvez | NVIDIA | Speaker | Parameterized CUDA Graph Launch in PyTorch: CUDA Graphs Without the Pain | — |
| Daniel Holanda | AMD | Lightning talk | Lightning Talk: From Pretrained To Personal: Privacy-First Fine-Tuning on AI PCs | — |
| Danielle Pintz | Meta | Speaker | TorchStore: What We Learned Building Distributed Storage Solutions for AysncRL | — |
| Daniil Lyakhov | Intel | Poster | Poster Presentations: Inference & Production | — |
| Dave Grove | IBM | Poster | Poster Presentations: Inference & Production | — |
| Dave Grove | IBM | Lightning talk | Lightning Talk: Torch-Spyre: Compiling To a Multi-core Dataflow Accelerator With Inductor | — |
| David Brewster | Red Hat | Poster | Poster Presentations: GenAI & Multimodal | — |
| Deifilia Kieckhefen | Karlsruhe Institute of Technology | Lightning talk | Lightning Talk: Jigsaw: Domain and Tensor Parallelism for High-Resolution Input Training | — |
| Dev Jadhav | ING | Speaker | DualPipe from Scratch: Implementing DeepSeek's 5D Parallelism in PyTorch | — |
| Dmitriy Pastushenkov | Intel | Lightning talk | Lightning Talk: Full-Stack PyTorch Robotics VLA: From Data To Edge Via ExecuTorch/OpenVINO | — |
| Doug Smith | Red Hat | Speaker | Lights, Camera, Inference! Video Generation as a Service With VLLM-Omni | — |
| Driss Guessous | Meta | Lightning talk | Lightning Talk: FlexAttention + FlashAttention-4: Fast and Flexible | — |
| Driss Guessous | Meta | Speaker | Meet the Developers of PyTorch Module Maintainers | — |
| Edward Yang | Meta | Speaker | Meet the Developers of PyTorch Module Maintainers | — |
| Ekin Karabulut | NVIDIA | Speaker | Beyond the Theory: What Actually Breaks When You Scale Your Disaggregated Pytorch Models | — |
| Elias Ellison | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Elias Ellison | Meta | Lightning talk | Lightning Talk: Combo Kernels: Horizontal Fusion Optimization in Torch.compile | — |
| Elias Ellison | Meta | Lightning talk | Lightning Talk: Faster Than SOTA Kernels in Torch.compile With Subgraph Fusions and Custom Op Autotuning | — |
| Eustache Le Bihan | Hugging Face | Lightning talk | Lightning Talk: Enabling the Audio Modality for Language Models | — |
| Fadi Arafeh | Arm | Speaker | Optimizing CPU LLM Inference in PyTorch: Lessons From VLLM | — |
| Felix Marty | AMD | Poster | Poster Presentations: Inference & Production | — |
| Gabrielle US | NVIDIA | Speaker | Birds of A Feather: NCCL in the Wild: Scaling Communications To Thousands of GPUs | — |
| George Gekov | Arm | Lightning talk | Lightning Talk: Running ExecuTorch Applications With Silicon Acceleration, in Ultra-low Power | — |
| Greg Pereira | Red Hat | Lightning talk | Lightning Talk: Beyond Generic Spans: Distributed Tracing for Actionable LLM Observability | — |
| Grzegorz Chlebus | NVIDIA | Speaker | The Science and Practice of Open and Scalable LLM Evaluations | — |
| Haichen Zhang | AMD | Lightning talk | Lightning Talk: Training Embedding Model Resiliently for Multimodal Model Inference Routing | — |
| Hameer Abbasi | Inc | Speaker | Accelerating Complex-Valued Tensors With Torch.compile | — |
| Hang Yin | Alibaba Cloud | Speaker | Birds of A Feather: Disaggregated Tokenization: Building Toward Tokens-In-Tokens-Out LLM Inference | — |
| Harshita Varma | Independent | Poster | Poster Presentations: Frameworks & Compilers | — |
| Huamin Chen | Microsoft | Lightning talk | Lightning Talk: Training Embedding Model Resiliently for Multimodal Model Inference Routing | — |
| hyunkyun moon | Moreh | Speaker | Birds of A Feather: Disaggregated Tokenization: Building Toward Tokens-In-Tokens-Out LLM Inference | — |
| hyunkyun moon | Moreh | Lightning talk | Lightning Talk: Not All Tokens Are Equal: Semantic KV-Cache for Agentic LLM Serving | — |
| Iswarya Alex | AMD | Lightning talk | Lightning Talk: From Pretrained To Personal: Privacy-First Fine-Tuning on AI PCs | — |
| Ivan Kobzarev | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Jan van Lunteren | IBM | Speaker | Portable High‑Performance LLM Serving: A Triton Backend for VLLM | — |
| Jana van Greunen | Meta | Speaker | Sponsored Session: TorchTPU: Expanding TPU Programmability to Pytorch | — |
| Jane Xu | Meta | Speaker | How To Write C++ Extensions in 2026 | — |
| Jason Ansel | Meta | Speaker | Meet the Developers of Helion | — |
| Jason Ansel | Meta | Speaker | Meet the Developers of PyTorch Module Maintainers | — |
| Jason Zhihuai Zhu | Arm | Lightning talk | Lightning Talk: Accelerating On-Device ML Inference With ExecuTorch and Arm SME2 | — |
| Jeff Hammond | NVIDIA | Speaker | Birds of A Feather: NCCL in the Wild: Scaling Communications To Thousands of GPUs | — |
| Joshua Lochner | Hugging Face | Speaker | Deploying PyTorch Models To the Browser and Beyond With Transformers.js | — |
| Julien Demouth | NVIDIA | Speaker | Optimizing Large MoE Inference on NVIDIA Blackwell: NVFP4, ADP, and DualPipe Strategies | — |
| Karthick Panner Selvam | Meta | Lightning talk | Lightning Talk: Combo Kernels: Horizontal Fusion Optimization in Torch.compile | — |
| Kartikey Rawat | Qualcomm | Speaker | On-Device LLM Inference on Android With ExecuTorch and Qualcomm QNN | — |
| Kashif Rasul | Hugging Face | Speaker | From Responses To Trajectories: Multi-Turn and Multi-Environment Reinforcement Learning | — |
| Kat Ko | Speaker | Sponsored Session: TorchTPU: Expanding TPU Programmability to Pytorch | — | |
| Kateryna Romashko | Red Hat | Speaker | From Gradients To Governance: Making PyTorch Lineage-Aware | — |
| Ke Wen | NVIDIA | Speaker | PyTorch Symmetric Memory + NCCL Device APIs: A New Path Towards Multi-GPU Kernels | — |
| Ke Wen | NVIDIA | Speaker | Birds of A Feather: NCCL in the Wild: Scaling Communications To Thousands of GPUs | — |
| Kellen Swain | Speaker | The Token Slice: Implementing Preemptive Scheduling Via Chunked Decoding | — | |
| Kshiteej Kalambarkar | NVIDIA | Speaker | Seamless Integration: Custom Kernels in the Torch.compile Stack Without Graphbreaks | — |
| Lars Heyen | Karlsruhe Institute of Technology | Lightning talk | Lightning Talk: Bayesian Neural Networks With Variational Inference in PyTorch | — |
| Liz Li | AMD | Speaker | Bringing PyTorch Monarch to AMD GPUs: Single-Controller Distributed Training on ROCm | — |
| Luca Wehrstedt | Meta | Speaker | Fp8 Training From Hopper To Blackwell | — |
| Lucas Pasqualin | PyTorch | Speaker | TorchStore: What We Learned Building Distributed Storage Solutions for AysncRL | — |
| Ludovic Henry | Rivos | Speaker | PyTorch on RISC-V: From Cross-Compilation To Native CI | — |
| Maciej Szymkowski | Future Processing | Lightning talk | Lightning Talk: Ball Tracking and Detection in Soccer Videos - Comparison of VLMs and Traditional Pipelines | — |
| Madhav Bissa | Speaker | Beyond JSON-RPC: Scaling Model Context Protocols With gRPC in the PyTorch Ecosystem | — | |
| Maen Suleiman | Amazon Web Services | Lightning talk | Lightning Talk: Pluggable PyTorch LLM Inference Architecture With VLLM and AWS Neuron Backends | — |
| Maen Suleiman | Amazon Web Services | Lightning talk | Lightning Talk: Distributed AI Without the Infrastructure Tax | — |
| Marcus Edel | Collabora | Poster | Poster Presentations: Inference & Production | — |
| Marius Eriksen | Meta | Lightning talk | Lightning Talk: Monarch: An API To Your Supercomputer | — |
| Markus Hoehnerbach | Meta | Speaker | Meet the Developers of Helion | — |
| Maroon Ayoub | IBM | Speaker | Birds of A Feather: Disaggregated Tokenization: Building Toward Tokens-In-Tokens-Out LLM Inference | — |
| Maroon Ayoub | IBM | Speaker | Why WideEP Inference Needs Data-Parallel-Aware Scheduling | — |
| Maroon Ayoub | IBM | Lightning talk | Lightning Talk: KV-Cache Centric Inference: Building a State-Aware Serving Platform With Llm-d and VLLM | — |
| Maroon Ayoub | IBM | Speaker | The Token Slice: Implementing Preemptive Scheduling Via Chunked Decoding | — |
| Maroon Ayoub | IBM | Lightning talk | Lightning Talk: Not All Tokens Are Equal: Semantic KV-Cache for Agentic LLM Serving | — |
| Martin Hickey | IBM | Lightning talk | Lightning Talk: KV-Cache Centric Inference: Building a State-Aware Serving Platform With Llm-d and VLLM | — |
| Martin Pavella | NXP Semiconductors | Lightning talk | Lightning Talk: Flexible Deployment of PyTorch Models on MCU-Class Devices Using ExecuTorch | — |
| Maryam Tahhan | Red Hat | Lightning talk | Lightning Talk: Slash LLM Cold-Start Times by Pre-distributing GPU Caches | — |
| Masaki Kozuki | NVIDIA | Speaker | Seamless Integration: Custom Kernels in the Torch.compile Stack Without Graphbreaks | — |
| Matej Sirovatka | Prime Intellect | Lightning talk | Lightning Talk: Trinity Large - Torchtitan on 2000+ B300s | — |
| Mathew Odden | Red Hat | Poster | Poster Presentations: Inference & Production | — |
| Matthias Cremon | Meta | Lightning talk | Lightning Talk: ExecuTorch on Microcontrollers: Deploying PyTorch To the Smallest Edge | — |
| Matthias Jouanneaux | NVIDIA | Speaker | Optimizing PyTorch on CPU-GPU Coherent Platforms | — |
| Maurits de Groot | Nebius | Speaker | Sponsored Session: Fault-Tolerant Training: How We Build Reliable Clusters for Distributed AI Workloads | — |
| Mergen Nachin | Meta | Speaker | Meet the Developers of PyTorch Module Maintainers | — |
| Mergen Nachin | Meta | Speaker | Bringing ExecuTorch To the Next Frontiers of Edge AI | — |
| Michael Lazos | Meta | Speaker | Enabling State-of-the-art Asynchronous Execution in Torch.compile With CUDA Streams | — |
| Michelle Audirac | Harvard University | Poster | Poster Presentations: Applications & Case Studies | — |
| Mikayla Gawarecki | Meta | Speaker | How To Write C++ Extensions in 2026 | — |
| Mike Krom | ACF Cybersolutions | Lightning talk | Lightning Talk: Live Migration of PyTorch GPU Nodes From Azure To European Clouds | — |
| Mingxuan Zhao | IBM | Speaker | Teaching PyTorch To Read Your Worst PDFs With Docling | — |
| Minh Hai Nguyen | Toulouse University | Lightning talk | Lightning Talk: How DeepInverse Is Solving Imaging in Science and Healthcare With PyTorch | — |
| Mohaddisa Maryam | Neurosonic Academy | Lightning talk | Lightning Talk: Bridging the Gap: Engineering Compliant "Glass Box" Medical AI With PyTorch | — |
| Molly Xu | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Muhammad Saqib Hussain | ClinExplain | Lightning talk | Lightning Talk: Bridging the Gap: Engineering Compliant "Glass Box" Medical AI With PyTorch | — |
| Natalia Gimelshein | Meta | Speaker | Meet the Developers of PyTorch Module Maintainers | — |
| Nicolò Lucchesi | Red Hat | Speaker | Meet the vLLM Maintainers | — |
| Nicolò Lucchesi | Red Hat | Lightning talk | Lightning Talk: Inside VLLM's KV Offloading Connector: Async Memory Transfers for Higher Inference Throughput | — |
| Nikhil Patel | Meta | Lightning talk | Lightning Talk: Achieving SOTA GEMM Performance: A CuTeDSL Backend for PyTorch Inductor | — |
| Nikita Verma | Individual | Poster | Poster Presentations: Frameworks & Compilers | — |
| Nili Guy | IBM | Speaker | Birds of A Feather: Disaggregated Tokenization: Building Toward Tokens-In-Tokens-Out LLM Inference | — |
| Oguz Ulgen | Meta | Speaker | Helion 1.0: A High-Level DSL for Performance Portable Kernels | — |
| Oguz Ulgen | Meta | Speaker | Meet the Developers of Helion | — |
| Olivier Tardieu | IBM | Lightning talk | Lightning Talk: Torch-Spyre: Compiling To a Multi-core Dataflow Accelerator With Inductor | — |
| Olivier Tardieu | IBM | Poster | Poster Presentations: Inference & Production | — |
| Owen O'Donnell | OWL Integrations | Lightning talk | Lightning Talk: Deep Learning in the Wild: Embedded PyTorch for Real-World Conservation Bioacoustics | — |
| Pablo Monteagudo Lago | AMD | Speaker | Brevitas Quantization Library | — |
| Parul Singh | Red Hat | Speaker | Why Classic IAM Collapses for Agents: Rethinking IAM for Agentic Systems | — |
| Paul Zhang | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Paul Zhang | Meta | Lightning talk | Lightning Talk: Faster Than SOTA Kernels in Torch.compile With Subgraph Fusions and Custom Op Autotuning | — |
| Paula Mesa Macias | Pau&Company | Lightning talk | Lightning Talk: Ethical, Privacy and Sustainability Considerations in PyTorch Systems | — |
| Pavani Rajula | NeuCorelytix Solutions LLP | Poster | Poster Presentations: Responsible AI & Compliance | — |
| Pavel Macenauer | NXP Semiconductors | Lightning talk | Lightning Talk: Every Millisecond Counts: The Fine-tuning Journey of an Ultra-Efficient PyTorch Model for the Edge | — |
| Pawel Gadzinski | NVIDIA | Speaker | Seamless Integration: Custom Kernels in the Torch.compile Stack Without Graphbreaks | — |
| Pedro Cuenca | Hugging Face | Speaker | Write Once, Run Everywhere with Pytorch Transformers | — |
| Peter Staar | IBM | Speaker | Teaching PyTorch To Read Your Worst PDFs With Docling | — |
| Pierre Quinton | EPFL | Lightning talk | Lightning Talk: TorchJD: Jacobian Descent in PyTorch | — |
| Raja Gopal Hari Vijay - | Zoho | Speaker | Building Trust for Users and Regulators Alike: A Cost-Efficient PyTorch Path To Compliance-as-Code | — |
| Reza Rahimi | Yasp.ai | Lightning talk | Lightning Talk: Coding Agents for Compiler Construction: Beyond the AI Assistant Paradigm | — |
| Ricardo Noriega | Red Hat | Speaker | Lights, Camera, Inference! Video Generation as a Service With VLLM-Omni | — |
| Richard Zou | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| RJ Ascani | Meta | Lightning talk | Lightning Talk: ExecuTorch on Microcontrollers: Deploying PyTorch To the Smallest Edge | — |
| Robert Kalmar | NXP Semiconductors | Lightning talk | Lightning Talk: Flexible Deployment of PyTorch Models on MCU-Class Devices Using ExecuTorch | — |
| Robert King | Chronosphere | Lightning talk | Lightning Talk: Building AI That Ops Teams Actually Trust | — |
| Romeo Kienzler | IBM | Lightning talk | Lightning Talk: TerraKit: Standardising AI-Ready Geospatial Data Preparation for the TorchGeo Ecosystem | — |
| Ron Kahn | NVIDIA | Speaker | Beyond the Theory: What Actually Breaks When You Scale Your Disaggregated Pytorch Models | — |
| Rosheen Naeem | Miro | Lightning talk | Lightning Talk: Why Your Forecasting Transformer Isn’t Working (And How To Fix It in Python) | — |
| Rosie Lickorish | IBM | Lightning talk | Lightning Talk: TerraKit: Standardising AI-Ready Geospatial Data Preparation for the TorchGeo Ecosystem | — |
| Roy Saurabh | AffectLog | Speaker | Birds of A Feather: Engineering for the EU AI Act: What Should PyTorch Expose Natively? | — |
| Rudraksh Karpe | Simplismart | Poster | Poster Presentations: Inference & Production | — |
| Sahana Venkatesh | Wayve | Lightning talk | Lightning Talk: Why Logging Isn’t Enough: Making PyTorch Training Regressions Visible in Practice | — |
| Sally O'Malley | OpenAI | Lightning talk | Lightning Talk: Beyond Generic Spans: Distributed Tracing for Actionable LLM Observability | — |
| Samaresh Kumar Singh | HP | Poster | Poster Presentations: Inference & Production | — |
| Samet Akcay | Intel | Lightning talk | Lightning Talk: Full-Stack PyTorch Robotics VLA: From Data To Edge Via ExecuTorch/OpenVINO | — |
| Sanket Purandare | Meta | Lightning talk | Lightning Talk: Graph Based Pipeline Parallelism | — |
| Sayak Paul | Hugging Face | Speaker | torch.compile and Diffusers: A Hands-On Guide to Peak Performance | — |
| Sergio Paniego Blanco | Hugging Face | Speaker | From Responses To Trajectories: Multi-Turn and Multi-Environment Reinforcement Learning | — |
| Shivay Lamba | CNCF Ambassador | Poster | Poster Presentations: Inference & Production | — |
| Shivay Lamba | CNCF Ambassador | Speaker | On-Device LLM Inference on Android With ExecuTorch and Qualcomm QNN | — |
| Simon Fan | Meta | Lightning talk | Lightning Talk: Graph Based Pipeline Parallelism | — |
| Songlin Jiang | Aalto University | Speaker | Optimizing Reinforcement Learning at Trillion-Parameter Scale | — |
| Stefan Krassin | Yasp.ai | Lightning talk | Lightning Talk: Coding Agents for Compiler Construction: Beyond the AI Assistant Paradigm | — |
| Suraj Muraleedharan | Amazon Web Services | Lightning talk | Lightning Talk: Cross-Region Model Serving: PyTorch Inference, Observability & LLMOps | — |
| Suriyaa MM | Indian Institute of Technology Tirupati | Poster | Poster Presentations: Inference & Production | — |
| Sylvain Jeaugey | NVIDIA | Speaker | PyTorch Symmetric Memory + NCCL Device APIs: A New Path Towards Multi-GPU Kernels | — |
| Taraqur Rahman | OWL Integrations | Lightning talk | Lightning Talk: Deep Learning in the Wild: Embedded PyTorch for Real-World Conservation Bioacoustics | — |
| Thomas Ortner | IBM | Lightning talk | Lightning Talk: Building a PyTorch‑native VLLM Plugin for IBM Spyre - Thomas Parnell, IBM Research & Thomas Ortner, IBM Research Europe - Zurich | — |
| Thomas Parnell | IBM | Lightning talk | Lightning Talk: Building a PyTorch‑native VLLM Plugin for IBM Spyre - Thomas Parnell, IBM Research & Thomas Ortner, IBM Research Europe - Zurich | — |
| Thomas Viehmann | Lightning AI | Lightning talk | Model-Changing Transforms With Torch.compile | — |
| Trinadh Kotturu | Lightning talk | Lightning Talk: Bringing Google’s Colossus to PyTorch: Rapid Storage via fsspec to Keep GPUs Busy | — | |
| Tristan Rice | Meta | Lightning talk | Lightning Talk: Debugging the Undebuggable: Introducing Torch.distributed.debug | — |
| Tyler Michael Smith | Red Hat | Speaker | Meet the vLLM Maintainers | — |
| Tyler Michael Smith | Red Hat | Speaker | Why WideEP Inference Needs Data-Parallel-Aware Scheduling | — |
| Valeri Milke | VamiSec GmbH | Speaker | Securing Agentic AI With PyTorch: Threat Modeling & LLM Red Teaming in Practice | — |
| Valérian Rey | Simplex Lab | Lightning talk | Lightning Talk: TorchJD: Jacobian Descent in PyTorch | — |
| Vijay Janapa Reddi | Harvard University | Speaker | Build PyTorch to Understand PyTorch | — |
| Vineet Suryan | Collabora | Poster | Poster Presentations: Inference & Production | — |
| Weiyi Wang | Lightning talk | Lightning Talk: From Hugging Face To Handheld: Scaling LLM Deployment With LiteRT Generative API | — | |
| Will Feng | Meta | Speaker | Meet the Developers of Helion | — |
| William Wen | Meta | Poster | Poster Presentations: Frameworks & Compilers | — |
| Xi Ning Wang | Alibaba Cloud | Speaker | Birds of A Feather: Disaggregated Tokenization: Building Toward Tokens-In-Tokens-Out LLM Inference | — |
| Yahav Biran | Amazon Web Services | Lightning talk | Lightning Talk: Pluggable PyTorch LLM Inference Architecture With VLLM and AWS Neuron Backends | — |
| Yahav Biran | Amazon Web Services | Lightning talk | Lightning Talk: Distributed AI Without the Infrastructure Tax | — |
| Zachary Streeter | AMD | Speaker | Bringing PyTorch Monarch to AMD GPUs: Single-Controller Distributed Training on ROCm | — |
| Zain Huda | Meta | Lightning talk | Lightning Talk: Scaling Recommendation Systems To 2K GPUs and Beyond | — |
Sources
- https://events.linuxfoundation.org/pytorch-conference-europe/
- https://events.linuxfoundation.org/pytorch-conference-europe/attend/venue-travel/
- https://events.linuxfoundation.org/pytorch-conference-europe/register/
- https://pytorchconferenceeu2026.sched.com/list/descriptions/
- https://pytorch.org/blog/pytorch-conference-europe-2026-a-landmark-moment-for-open-source-ai-in-paris/