All talks from PyTorch Conference Europe 2026

0:53

1

What PyTorch Conference Europe 2026 Was Really Like – Official PyTorchCon EU Highlights | Paris

This talk captures the vibrant atmosphere of a tech conference focused on artificial intelligence in...

9:50

2

Lightning Talk: How DeepInverse Is Solving Imaging in Science and H... Andrew Wang & Minh Hai Nguyen

This talk covers the significant application of AI in imaging, particularly through the lens of comp...

25:37

3

Why WideEP Inference Needs Data-Parallel-Aware Scheduling - Maroon Ayoub & Tyler Michael Smith

This talk covers the development and functionality of LMD, a distributed large model inference frame...

19:17

4

Write Once, Run Everywhere with Pytorch Transformers - Pedro Cuenca, Hugging Face

This talk discusses how the Transformers library serves as a reference implementation for various do...

21:08

5

Build PyTorch to Understand PyTorch - Vijay Janapa Reddi & Andrea Mattia Garavagno

This talk introduces Tiny Torch, an educational project designed to help learners build their own ma...

9:51

6

Lightning Talk: Why Your Forecasting Transformer Isn’t Working (And How To Fix It... Rosheen Naeem

In this talk, Roshni Naeem discusses challenges in forecasting solar energy generation using a trans...

12:20

7

Lightning Talk: Bayesian Neural Networks With Variational Inference in PyTorch - Lars Heyen

This talk discusses the integration of AI and uncertainty quantification in weather forecasting. The...

11:15

8

Lightning Talk: TerraKit: Standardising AI-Ready Geospatial Data... Rosie Lickorish & Romeo Kienzler

This talk introduces Terra Kit, an open source Python library designed for the curation of geospatia...

10:24

9

Lightning Talk: ExecuTorch on Microcontrollers: Deploying PyTorch To... RJ Ascani & Matthias Cremon

This talk covers Executive Torch, a framework developed by Meta for deploying PyTorch models on micr...

28:38

10

Enabling State-of-the-art Asynchronous Execution in Torch.compile With CUDA Streams - Michael Lazos

This talk discusses the integration of state-of-the-art asynchronous execution in Torch Compile usin...

22:13

11

The Token Slice: Implementing Preemptive Scheduling Via Chunked Decod... Maroon Ayoub & Kellen Swain

This talk discusses the 'token slice' and its role in improving AI workload scheduling through the L...

10:06

12

Lightning Talk: Deep Learning in the Wild: Embedded PyTorch for... Taraqur Rahman & Owen O'Donnell

This talk presents a project focused on deep learning applications in remote environments, specifica...

26:53

13

The Science and Practice of Open and Scalable LLM Evaluations - Grzegorz Chlebus, NVIDIA

This talk focuses on evaluation methodologies at Nvidia, specifically around benchmarking machine le...

24:58

14

Lights, Camera, Inference! Video Generation as a Service With VLLM-O... Ricardo Noriega & Doug Smith

This talk presents VLLM Omni, a powerful inference server designed for multimodal model serving, whi...

20:31

15

Keynote: PyTorch Updates - Edward Yang, Research Engineer, Meta

In this keynote presentation at the first PyTorch conference in Europe, Edward, a core maintainer fr...

24:01

16

Tour De Force: LLM Inference Optimization From Simple To Sophisticated - Christin Pohl, Microsoft

This talk covers LLM inference optimization with a focus on self-hosting models and the consideratio...

9:57

17

Lightning Talk: Bringing Google’s Colossus to PyTorch: Rapid Stor... Ankita Luthra & Trinadh Kotturu

This talk discusses the integration of Google Cloud Storage file system with PyTorch, addressing the...

24:27

18

Helion 1.0: A High-Level DSL for Performance Portable Kernels - Oguz Ulgen, Meta

This talk introduces Helium, a new domain-specific language (DSL) for kernel authoring that simplifi...

24:53

19

torch.compile and Diffusers: A Hands-On Guide to Peak Performance - Sayak Paul, Hugging Face

In this talk, the speaker, Shyuk, a research engineer at Hugging Face, discusses the integration of...

27:00

20

Optimizing PyTorch on CPU-GPU Coherent Platforms - Matthias Jouanneaux, Nvidia

In this session, Matthias, a software engineer for PyTorch at Nvidia, discusses the optimization of...

10:23

21

Lightning Talk: Jigsaw: Domain and Tensor Parallelism for High-Resolution Inp... Deifilia Kieckhefen

This talk presents Jigsaw, a domain and tensor parallel technique aimed at optimizing the training o...

24:33

22

Why Classic IAM Collapses for Agents: Rethinking IAM for Agentic Systems - Parul Singh, Red Hat

This talk examines the limitations of classic identity management (IM) in accommodating modern auton...

33:07

23

Securing Agentic AI With PyTorch: Threat Modeling & LLM Red Teaming in Practice - Valeri Milke

In this session, Sabre Milka discusses the various risks associated with artificial intelligence (AI...

11:53

24

Keynote: Co-Evolution: How the Open Source Intelligence Stack Compounds - Mark Collier

This talk marks the inaugural PyTorch conference in Europe and emphasizes the significance of the Py...

8:54

25

Lightning Talk: Implementing Single-Dim Strategies With Sharding Validator - Anshul Sinha, Meta

This talk focuses on implementing single dimension strategies using the strategy validator in DTenso...

12:13

26

Keynote: Community Led Open Source RL - Joe Spisak

This talk is presented by Joe, a significant contributor to the development of PyTorch, and now a ke...

14:20

27

Lightning Talk: Flexible Deployment of PyTorch Models on MCU-Class... Robert Kalmar & Martin Pavella

In this session, Robert Calmar discusses the flexible deployment of PyTorch models on microcontrolle...

9:54

28

Lightning Talk: TorchJD: Jacobian Descent in PyTorch - Pierre Quinton & Valérian Rey

This talk introduces Torch JD, a library designed for multi-objective optimization using PyTorch, fo...

15:39

29

Lightning Talk: Cross-Region Model Serving: PyTorch Inference, Observability... Suraj Muraleedharan

This talk covers cross-region model serving and multi-region model inference from a customer perspec...

11:41

30

Lightning Talk: Coding Agents for Compiler Construction: Beyond the... Reza Rahimi & Stefan Krassin

This talk features Reza, the CTO of YASSP, as he presents on coding agents for compiler construction...

24:34

31

Optimizing Reinforcement Learning at Trillion-Parameter Scale - Songlin Jiang

This talk covers the application of reinforcement learning on trillion parameter large language mode...

23:12

32

Sponsored Session: TorchTPU: Expanding TPU Programmabil... Kat Ko, Claudio Basile & Jana van Greunen

This talk introduces Torch TPU, a new platform that enhances PyTorch's compatibility with various ha...

12:48

33

Lightning Talk: Training Embedding Model Resiliently for Multimodal M... Huamin Chen & Haichen Zhang

This talk covers the importance and functionalities of a semantic router in model inference for appl...

9:46

34

Lightning Talk: Ethical, Privacy and Sustainability Considerations in PyTorch S... Paula Mesa Macias

In this session, Paula Mesa Matias discusses ethical privacy and sustainability considerations in Py...

30:20

35

Brevitas Quantization Library - Pablo Monteagudo Lago, AMD

This talk covers Brevitas, a neural network quantization framework in PyTorch developed by AMD. The...

4:45

36

Sponsored Keynote: From One Node to Distributed Training and Inference. How the PyTo... Ramine Roane

This talk covers the evolution of PyTorch from simplifying single GPU programming to becoming the pr...

24:23

37

Teaching PyTorch To Read Your Worst PDFs With Docling - Mingxuan Zhao, Peter Staar & Carol Chen

This talk introduces Dog, an open-source Python library developed by IBM that focuses on efficient d...

12:41

38

Lightning Talk: Running ExecuTorch Applications With Silicon Accelera... George Gekov & Aki Makkonen

This talk focuses on running executor applications with silicon acceleration for ultra low power dev...

9:42

39

Lightning Talk: From Pretrained To Personal: Privacy-First... Daniel Holanda Noronha & Iswarya Alex

This talk focuses on fine-tuning machine learning models on AMD's AIPCs, highlighting the benefits o...

11:15

40

Keynote: The Unbearable Lightness of (Agentic) Evaluations - Besmira Nushi

This talk focuses on the evolution of AI evaluation over the last three years, detailing the shift i...

26:01

41

Parameterized CUDA Graph Launch in PyTorch: CUDA Graphs Without the Pain - Daniel Galvez, NVIDIA

In this talk, Daniel Galvez discusses the enhancement of CUDA APIs within PyTorch, focusing specific...

25:23

42

On-Device LLM Inference on Android With ExecuTorch and Qualcomm QNN - Shivay Lamba & Kartikey Rawat

This talk introduces ExecuTorch, an on-device AI framework that enables the running of PyTorch model...

4:15

43

Sponsored Keynote: Any [ Agent | Model | Accelerator | Cloud ]... Maryam Tahhan & Nicolò Lucchesi

This talk focuses on Red Hat's strategy and initiatives in the realm of open-source artificial intel...

9:57

44

Lightning Talk: Combo Kernels: Horizontal Fusion Optimizat... Karthick Panner Selvam & Elias Ellison

This talk discusses combo kernels and horizontal fusion optimization in the context of torch.compile...

24:07

45

How To Write C++ Extensions in 2026 - Jane Xu, Meta & Mikayla Gawarecki, Meta

This talk discusses how to create a C++ custom extension for PyTorch in 2026, emphasizing the proces...

10:31

46

Lightning Talk: Inside VLLM's KV Offloading Connector: Async Memory Transfers for... Nicolò Lucchesi

This talk covers native key-value (KV) cache offloading in the VLM project, which is recognized as t...

11:30

47

Lightning Talk: Accelerating On-Device ML Inference With ExecuTorch and Arm SME2 - Jason Zhu, Arm

This talk presents the collaborative work of Tyler Mullen and Jason Zoo from ARM, focusing on enhanc...

23:25

48

From Responses To Trajectories: Multi-Turn and Multi-Environ... Kashif Rasul & Sergio Paniego Blanco

This talk, presented by Kashif and Sergio from Hugging Face, focuses on the evolution of reinforceme...

12:54

49

Lightning Talk: From Hugging Face To Handheld: Scaling LLM Deployment W... Cormac Brick & Weiyi Wang

This talk focuses on deploying Hugging Face transformer-like language models to edge devices using L...

20:51

50

Beyond the Theory: What Actually Breaks When You Scale Your Disaggregat... Ekin Karabulut & Ron Kahn

This talk discusses the challenges of scaling disaggregated PyTorch models in production environment...

20:16

51

Bringing PyTorch Monarch to AMD GPUs: Single-Controller Distributed Tra... Liz Li & Zachary Streeter

This talk covers the collaboration between AMD and Meta to enable single-controller distributed trai...

11:53

52

Lightning Talk: Why Logging Isn’t Enough: Making PyTorch Training Regressions Vi... Sahana Venkatesh

In this session, Sahana from Wave discusses the challenges of training models at scale, particularly...

10:24

53

Lightning Talk: Graph Based Pipeline Parallelism - Sanket Purandare, Meta & Simon Fan, Meta PyTorch

This talk introduces graph-based pipeline parallelism, a novel approach developed by the Meta PyTorc...

9:38

54

Lightning Talk: Ball Tracking and Detection in Soccer Videos - Comparison of... Maciej Szymkowski

In this lightning talk, Mati Sumkowski from Future Processing discusses his research focused on ball...

11:48

55

Lightning Talk: Backpropagation-Free Optimization in PyTorch - Andrii Krutsylo

This talk discusses gradient-free optimization methods applied in deep learning, particularly implem...

13:54

56

Lightning Talk: Every Millisecond Counts: The Fine-tuning Journey of an Ultra-Eff... Pavel Macenauer

This talk discusses the integration of the PyTorch ecosystem with NXP chips, which range from microc...

23:14

57

Bringing ExecuTorch To the Next Frontiers of Edge AI - Mergen Nachin, Meta

This talk focuses on the future of artificial general intelligence (AGI) and the role of Executor in...

14:04

58

Lightning Talk: Full-Stack PyTorch Robotics VLA: From Data To... Samet Akcay & Dmitriy Pastushenkov

This talk introduces a new full stack robotics vision language action model-based framework develope...

6:47

59

Lightning Talk: Slash LLM Cold-Start Times by Pre-distributing GPU... Billy McFall & Maryam Tahhan

This talk covers strategies for reducing the startup time of large language model (LLM) deployments...

10:27

60

Lightning Talk: Not All Tokens Are Equal: Semantic KV-Cache for Agen... Maroon Ayoub & Hyunkyun Moon

This talk explores the next set of optimizations in KV cache-centric inference, building on concepts...

10:08

61

Lightning Talk: Debugging the Undebuggable: Introducing Torch.distributed.debug - Tristan Rice

This talk presents improvements to debugging in PyTorch distributed, emphasizing the new 'torch dist...

10:23

62

Lightning Talk: KV-Cache Centric Inference: Building a State-Aware... Maroon Ayoub & Martin Hickey

This talk covers the critical concepts of service level objectives (SLOs) in AI inferencing, focusin...

9:21

63

Lightning Talk: FlexAttention + FlashAttention-4: Fast and Flexible - Driss Guessous, Meta

In this talk, TrisKus from Meta presents FlexAttention and FlashAttention V4, a new backend that sig...

11:35

64

Lightning Talk: Beyond Generic Spans: Distributed Tracing for Actio... Sally O'Malley & Greg Pereira

This talk covers the integration of tracing into the LMD framework, a Kubernetes-native distributed...

10:01

65

Lightning Talk: Step-Aligned Telemetry for Distributed PyTorch Training (Time... Abhinav Srivastav

This talk discusses step aligned telemetrics for PyTorch distributed training, focusing on how to id...

21:35

66

Optimizing Large MoE Inference on NVIDIA Blackwell: NVFP4, ADP, and DualPipe Strat... Julien Demouth

This talk explores the optimization of Mixture of Experts (MOE) inference on NVIDIA's Blackwell arch...

7:26

67

Lightning Talk: Live Migration of PyTorch GPU Nodes From Azure To European Clouds - Mike Krom

This talk discusses the process of migrating PyTorch projects to European cloud providers, specifica...

14:03

68

Lightning Talk: Enabling the Audio Modality for Language Models - Eustache Le Bihan, Hugging Face

This talk focuses on how to integrate the audio modality into language models within the open source...

24:02

69

Optimizing CPU LLM Inference in PyTorch: Lessons From VLLM - Crefeda Rodrigues & Fadi Arafeh

This talk focuses on optimizing large model inference on CPU within the PyTorch ecosystem, specifica...

26:05

70

TorchStore: What We Learned Building Distributed Storage Sol... Lucas P, Danielle P, Allen W, Amir A

This talk introduces Torch Store, a solution for efficient weight synchronization in reinforcement l...

9:41

71

Lightning Talk: Scaling Recommendation Systems To 2K GPUs and Beyond - Zain Huda, Meta

This talk covers the scaling of recommendation systems using PyTorch, specifically focusing on techn...

15:55

72

De-mystifying PyTorch for ASICs: When (and Why) To Move Your Development To AI A... Alpha Romer Coma

This talk explores the transition of machine learning development from traditional GPUs to AI accele...

21:47

73

Model-Changing Transforms With Torch.compile - Thomas Viehmann, Lightning AI

In this talk, Tom Vman discusses model changing transforms using Torch Compile, highlighting the ben...

24:32

74

PyTorch Symmetric Memory + NCCL Device APIs: A New Path Towards Multi-GP... Ke Wen & Sylvain Jeaugey

This talk focuses on the integration of PyTorch symmetric memory with Nico device APIs, presenting a...

10:20

75

Lightning Talk: Building a PyTorch‑native VLLM Plugin for IBM Spyre - Thomas Parnell & Thomas Ortner

This talk delves into the integration of the Spire device with the VLLM plugin, which enhances perfo...

25:24

76

PyTorch on RISC-V: From Cross-Compilation To Native CI - Ludovic Henry, Meta

This talk explores the integration of PyTorch with RISC-V, highlighting the challenges and collabora...

5:12

77

Sponsored Keynote: Open Source Infrastructure for the AI Native Era - Jonathan Bryce

This talk discusses the growth of the PyTorch community and its relationship with the Cloud Native C...

15:37

78

Keynote: Gemma 4: Compacting Intelligence for the Edge - Léonard Hussenot

This talk explores the evolution of large language models (LLMs), highlighting the shift from pre-tr...

15:08

79

Keynote: Stream Everything - Moving from Request input to Streaming input - Patrick von Platen

In this talk, Patrick from Mistral discusses an innovative approach to streaming in language models...

10:21

80

Lightning Talk: Pluggable PyTorch LLM Inference Architecture With VLL... Yahav Biran & Maen Suleiman

In this talk, Yahav from the Anapuna ML team discusses the challenges of running machine learning mo...

10:17

81

Lightning Talk: Faster Than SOTA Kernels in Torch.compile With Subgrap... Elias Ellison & Paul Zhang

In this talk, Paul Zheng from the PyTorch team at Meta discusses advancements in subgraph fusions an...

21:29

82

From Gradients To Governance: Making PyTorch Lineage-Aware - Kateryna Romashko & Clodagh Walsh

This talk addresses the critical need for lineage awareness in PyTorch as it transitions into enterp...

11:28

83

Lightning Talk: Torch-Spyre: Compiling To a Multi-core Dataflow Accelerator... D. Grove & O. Tardieu

This talk covers the insights gathered by IBM's team regarding their implementation of PyTorch Induc...

24:28

84

Portable High‑Performance LLM Serving: A Triton Backend for... Burkhard Ringlein & Jan van Lunteren

This talk covers the introduction of a Triton backend for vLLM, which is becoming the industry stand...

8:40

85

Lightning Talk: Bridging the Gap: Engineering Compliant... Muhammad Saqib Hussain & Mohaddisa Maryam

This talk explores the intersection of artificial intelligence and clinical practice, highlighting t...

10:24

86

Keynote: The Hub as Infrastructure. From Open PyTorch Models, to a Safe and Perfor... Lysandre Debut

In this talk, Nander, the chief open source officer at Hugging Face, discusses the development of th...

22:49

87

Seamless Integration: Custom Kernels in the Torch.compile Stack Wi... Kshiteej K, Masaki K & Pawel G

This talk covers the integration of custom kernels into the torch compile stack, focusing on the col...

10:23

88

Keynote: PyTorch CTO - Matt White, Global CTO of AI, Linux Foundation

This talk provides an overview of the current state and growth of the PyTorch Foundation and its eco...

25:59

89

Deploying PyTorch Models To the Browser and Beyond With Transformers.js - Joshua Lochner

In this talk, Joshua, a machine learning engineer at Hugging Face, discusses his journey and the cre...

9:53

90

Lightning Talk: Building AI That Ops Teams Actually Trust - Robert King

In this talk, Rob King discusses building trustworthy AI solutions for operations teams, which often...

12:06

91

Lightning Talk: Distributed AI Without the Infrastructure Tax - Yahav Biran & Maen Suleiman

In this session, Yaav Biran, a customer engineer from the Annapurna ML team at Amazon, addresses the...

25:24

92

Fp8 Training From Hopper To Blackwell - Luca Wehrstedt, Meta

In this talk, the speaker discusses the implementation of FP8 training on Nvidia's latest GPUs, emph...

19:32

93

DualPipe from Scratch: Implementing DeepSeek's 5D Parallelism in PyTorch - Dev Jadhav, ING Bank

This talk focuses on the Dual Pipe implementation in PyTorch, presented by Theo Jadav, a tech lead m...

18:38

94

Building Trust for Users and Regulators Alike: A Cost-Efficient PyTorch Pat... Raja Gopal Hari Vijay

This talk focuses on the integration of compliance mechanisms within the Pyarch framework to address...

10:40

95

Keynote: vLLM & Ray Updates - Tyler Michael Smith & Artur Niederfahrenhorst

This talk covers advancements in vLLM, spearheaded by Tyler Smith, the chief architect of inference...

19:04

96

Beyond JSON-RPC: Scaling Model Context Protocols With gRPC in the Py... Ashesh Vidyut & Madhav Bissa

This talk explores the integration of GRPC with the Model Context Protocol (MCP) within the PyTorch...

7:45

97

Lightning Talk: Achieving SOTA GEMM Performance: A CuTeDSL Backend for PyTorch Induc... Nikhil Patel

This talk covers recent advancements in integrating QTSL gems into PyTorch Inductor to enhance perfo...

21:33

98

Bridging the Hardware Gap With Code Harnesses on the Hugging Face Kernels Hub - Ben Burtenshaw

This talk covers the integration of coding agents with custom kernels to address the growing demand...

21:15

99

Sponsored Session: Fault-Tolerant Training: How We Build Rel... Cyril Konkratenko & Maurits de Groot

This talk addresses the challenges of GPU cluster failures and presents strategies for minimizing su...

12:40

100

Accelerating Complex-Valued Tensors With Torch.compile - Hameer Abbasi, OpenTeams Inc.

This talk addresses the issue of compiling complex tensors in PyTorch, which currently cannot be com...

14:01

101

Lightning Talk: Accelerating PyTorch Models With Torch.compile's C++ Wrapper Mode - Bin Bao, Meta

This talk discusses the acceleration of PyTorch models using the torch.compile C++ wrapper mode pres...

10:34

102

Lightning Talk: Trinity Large - Torchtitan on 2000+ B300s - Matej Sirovatka, Prime Intellect

This talk focuses on the journey of pre-training a 400 billion parameter model from scratch, utilizi...

11:54

103

Lightning Talk: Monarch: An API To Your Supercomputer - Marius Eriksen, Meta

In this talk, Marius Eriksen presents Monarch, a project from Meta that provides a Pythonic API for...