De-mystifying PyTorch for ASICs: When to Move Your Dev to AI Accelerators
Details
✨ We’re bringing a PyTorch Conference Europe 2026 talk to the DEP AI Study Group!
We’re honored to feature a session presented at the PyTorch Conference Europe 2026: A Landmark Moment for Open Source AI, held in Paris last April 15. Now, we’re sharing it with the DEP community.
De-mystifying PyTorch for ASICs: When (and Why) to Move Your Development to AI Accelerators
GPU availability and rising costs are pushing ML teams to explore alternatives like Google TPUs and AWS Trainium. But is the shift worth it? This session provides a practical, code-first reality check on migrating PyTorch workloads to ASICs.
🔍 What to expect:
• Breakdown of compiler stacks: PyTorch/XLA (TPU) and TorchNeuron (Trainium)
• Understanding the “Compiler Tax” developers often encounter
• Side-by-side code comparisons and benchmarks using Llama 4, Gemma 3, Qwen 3, CNNs, and ViTs
💡 Key questions we will answer:
- How much rewriting is actually needed?
- Which models perform well on ASICs, and which struggle with dynamic shapes?
- What happens when debugging issues arise such as OOM or compilation hangs?
📊 Leave with a Migration Decision Matrix to help determine if your workload is ready for the ASIC leap.
👨💻 Speaker
Alpha Romer Coma
Multimodal AI Researcher | Cloud @ Kollab | 4x Microsoft, 4x Google, 2x AWS Certified
📅 May 9, 2026
⏰ 7:00 PM – 8:30 PM
📍 DEP Discord
See you there! 💻🔥