Wednesday's Demo Theater at #PyTorchCon features 13 presentations on training, inference, hardware enablement, and agentic workflows. All sessions are in the Community Expo on the Concourse Level.
10:40–10:50 AM: Maen Suleiman (@awscloud) demos reinforcement learning on Trainium e native PyTorch backend for AWS Trainium, with TorchTitan training, vLLM-Neuron generating rollouts, Monarch orchestrating, and TorchStore syncing weights.
10:55–11:05 AM: Ali Taha (@baseten) breaks down how Wan 2.2 generates a five-second video in under three seconds, a 53.6x improvement over baseline, using timestep distillation, custom kernel engineering, and four-bit quantization.
12:20–12:30 PM: Rain Jiang and Kevin Li (@verdacloud) demo WideEP for expert-parallel MoE inference on an NVIDIA NVL72 system, using a Rust-based frontend to run and monitor the workload.
12:35–12:45 PM: Tristan Webb (@dMatrix_AI) presents dmx-compressor, a PyTorch 2.0 based quantization framework that gives ML engineers numerical reference output to validate models running on ASIC hardware.
12:45–12:55 PM: Lin She (@clockworkio) demos surviving link and node failures in PyTorch training live, with LinkPass and TorchPass keeping the job running on the same training step.
1:00–1:10 PM: Ashok Emani and Qiacheng Li (@intel) show AI Playground Home Agent, which runs PyTorch AI workflows on an Intel AI PC and lets you use them from your phone.
1:15–1:25 PM: Zesheng Zong (@Huawei) presents CRCR (Cross-Repository CI Relay) for joint testing between PyTorch pull requests and accelerator backends, catching regressions at the PR phase.
1:30–1:40 PM: FNU Harsh and Sarah Ahmed (@LightningAI) demo an agent reproducing ML research on Lightning, showing where it succeeds autonomously and where it needs intervention.
1:45–1:55 PM: George Novack (@inferact) introduces TPU megakernels, with Kimi K3 megakernels running inside vLLM-TorchTPU.
2:00–2:10 PM: Jay Dawani (Lemurian Labs) examines why heterogeneous computing is so hard and what a hardware-agnostic stack has to get right.
2:15–2:25 PM: Minwook Ahn (@RebellionsAI) shares how extending PyTorch's device abstraction to support device-persistent tensors minimizes data movement between host and device.
2:30–2:40 PM: Jesse Treger (HPE) introduces OpenCLIC, an open inference communication framework for connectionless networks that is being integrated with vLLM.
3:55–4:05 PM: Mengjin Yan and Joshua Lee (@anyscalecompute) cover evolving Ray Core for post-training at scale, from scaling Ray's scheduling to 10,000-node clusters to moving PyTorch tensors directly between GPUs with Ray Direct Transport.
View the Demo Theater schedule: events.linuxfoundation.org/pytorch-confer…
10:40–10:50 AM: Maen Suleiman (@awscloud) demos reinforcement learning on Trainium e native PyTorch backend for AWS Trainium, with TorchTitan training, vLLM-Neuron generating rollouts, Monarch orchestrating, and TorchStore syncing weights.
10:55–11:05 AM: Ali Taha (@baseten) breaks down how Wan 2.2 generates a five-second video in under three seconds, a 53.6x improvement over baseline, using timestep distillation, custom kernel engineering, and four-bit quantization.
12:20–12:30 PM: Rain Jiang and Kevin Li (@verdacloud) demo WideEP for expert-parallel MoE inference on an NVIDIA NVL72 system, using a Rust-based frontend to run and monitor the workload.
12:35–12:45 PM: Tristan Webb (@dMatrix_AI) presents dmx-compressor, a PyTorch 2.0 based quantization framework that gives ML engineers numerical reference output to validate models running on ASIC hardware.
12:45–12:55 PM: Lin She (@clockworkio) demos surviving link and node failures in PyTorch training live, with LinkPass and TorchPass keeping the job running on the same training step.
1:00–1:10 PM: Ashok Emani and Qiacheng Li (@intel) show AI Playground Home Agent, which runs PyTorch AI workflows on an Intel AI PC and lets you use them from your phone.
1:15–1:25 PM: Zesheng Zong (@Huawei) presents CRCR (Cross-Repository CI Relay) for joint testing between PyTorch pull requests and accelerator backends, catching regressions at the PR phase.
1:30–1:40 PM: FNU Harsh and Sarah Ahmed (@LightningAI) demo an agent reproducing ML research on Lightning, showing where it succeeds autonomously and where it needs intervention.
1:45–1:55 PM: George Novack (@inferact) introduces TPU megakernels, with Kimi K3 megakernels running inside vLLM-TorchTPU.
2:00–2:10 PM: Jay Dawani (Lemurian Labs) examines why heterogeneous computing is so hard and what a hardware-agnostic stack has to get right.
2:15–2:25 PM: Minwook Ahn (@RebellionsAI) shares how extending PyTorch's device abstraction to support device-persistent tensors minimizes data movement between host and device.
2:30–2:40 PM: Jesse Treger (HPE) introduces OpenCLIC, an open inference communication framework for connectionless networks that is being integrated with vLLM.
3:55–4:05 PM: Mengjin Yan and Joshua Lee (@anyscalecompute) cover evolving Ray Core for post-training at scale, from scaling Ray's scheduling to 10,000-node clusters to moving PyTorch tensors directly between GPUs with Ray Direct Transport.
View the Demo Theater schedule: events.linuxfoundation.org/pytorch-confer…
Oct 21, 2026 · 8:01 PM UTC
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