Call for Papers
PyTorch Conference North America 2026 - POSTER SESSIONS
20 Oct 2026
21 Oct 2026
San Jose Convention Center San Jose, California, United States
Join us in San Jose, CA, October 20-21 for PyTorch Conference North America 2026. This two-day event hosted by the PyTorch Foundation gathers top-tier AI pioneers, researchers, and developers to explore the future of open source AI and the impact of PyTorch Foundation projects like PyTorch, vLLM, DeepSpeed, and Ray.
PyTorch Conference features in-depth technical talks, hands-on workshops, and candid conversations spanning the full AI stack, from bare metal infrastructure to applications and agent-based systems. The program features keynote sessions from leading voices in AI and practical deep dives on training, inference, GenAI, and focused tracks on responsible AI & compliance, security & privacy, frameworks & compilers, and more.
Call for Papers
31 Mar 2026
26 Jul 2026
CFP GUIDE
Please review our CFP Guide to answer many common questions before submitting.
PLEASE NOTE - Breakout Sessions and Poster Sessions are separate CFP’s for PyTorch North America 2026. To submit a talk for a breakout session please apply separately here.
DATES TO REMEMBER:
- Poster Session CFP Close: Sunday, July 26 at 11:59 pm PDT (UTC +-7)
- Poster Session Notifications: Monday, August 17, 2026
- Poster Sessions Announced: Tuesday, August 18, 2026
- Event Dates: Tuesday, October 20 - Wednesday, October 21, 2026
Reminder: This is a community event — so no product and/or vendor sales pitches.
CODE OF CONDUCT
By submitting, you agree to The Linux Foundation's Code of Conduct.
COMMITMENT TO INCLUSIVITY
Please review The Linux Foundation's Inclusive Speaker Orientation and Inclusive Language Initiative.
Please be mindful that The Linux Foundation does not allow any talk with 3 or more speakers to only have men participating in an effort to increase equity & inclusion.
PRIVACY POLICY
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Speaker personal information including name, company, job title, biography and photo will appear on the public schedule. For information on our privacy practices and commitment to protecting your privacy, please review our Privacy Policy.
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QUESTIONS?
Question about submitting a proposal? Contact us at cfp@linuxfoundation.org.
all submitted sessions
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Submitted sessions
- Adaptive Consensus for Low-Latency Distributed Systems
- Continuous Causal Inference for Real-Time Advertising Measurement
- Quantize-Then-Refine: Two-Stage Scoring for Memory-Efficient GPU Retrieval
- From Scheduled to Real Overlap: All-Gather/Reduce-Scatter in PyTorch Distributed
- Moral Tensors and DecisionProofs: Compiling Language into an Auditable, Grounded Safety Layer
- Atlas + Erebus: A Personal AI Research Platform on Two Prosumer GPUs
- Building Global Allies: How Male Mentors Can Accelerate Women in AI & Startups
- VLA Models on Intel Arc Pro: Enabling Real-Time Robot Control with PyTorch XPU and OpenVINO
- Stage-Specialized E-PD Disaggregation for SLO-Aware Multimodal Serving on Intel Arc Pro B50/B70
- AI in Analytics
- From PyTorch to a Phone: What Quantizes Cleanly On-Device, and What Doesn't
- Importance Aware Attention for Faster PyTorch Inference on Arm CPUs
- Training Embedding Models Resiliently for Multimodal Model Inference Routing
- Real-Time Ranking Systems for E-Commerce Search and Discovery
- Before the Loss Spike: Training Alignment for Precise PyTorch Debugging at Scale
- vLLM-plugin-FL -- Multi-Chip vLLM framework based on FlagOS backend
- Scaling Large-scale Distributed Training with DeepSpeed using Muon Optimizer
- KernelOpt: Multi-Agent GPU Kernel Optimizaton
- A Multi-Layer Profiling Toolkit for Out-of-Tree PyTorch Accelerators
- REBAR: Routing Extensions for Burst-Aware RL
- Efficient MoE LLM Inference on Arm with vLLM and OpenVINO
- Maestro: Benchmarking Concurrent GPU Operations as They Run in Real Workloads
- A Practical Taxonomy of LLM Inference Bottlenecks: Prefill, Decode, Memory, and Scheduling
- Improving PyTorch Efficiency on ARM with SVE-Accelerated Memory Primitives
- weights_only Was Supposed to Save You: PyTorch Model RCE in 2026 and Safe Loading
- Escape Hatches for torch.compile
- CUDA Graph on Large Scale Recommender Systems
- Train Across Your Entire GPU Fleet: Heterogeneous Mixed-Generation Training in Pure PyTorch
- "Powering AI/ML with Python and Apache iceberg"
- Breaking torch.distributed on Purpose: Chaos Engineering for Large-Scale PyTorch Training
- AllReduce, AllGather, or AllToAll? How Parallelism Shapes PyTorch Network Traffic
- TPU Model Performance Auto-optimization
- Diagnosing LLM Streaming Bottlenecks: Profiling TTFT, Decode Throughput, and Inference Telemetry
- Building Self-Healing Infrastructure for AI Agents
- The 11-Minute Cold Start: A Visual Anatomy of Why GPU Pods Take Forever to Become Useful
- Gradient Ghosts: Detecting Silent Numerical Failure in Mixed-Precision Training
- State Persistence in Containers
- The health benefits of a cheese based diet