Brussels / 31 January & 1 February 2026

schedule

AI Plumbers

Room: UD2.120 (Chavanne)
Calendar: iCal, xCal
Chat: Join the conversation!


09 10 11 12 13 14 15 16 17 18
Saturday Welcome to the AI Plumbers Devroom
 Multimodal support in llama.cpp - Achievements and Future Directions
 API Remoting for llama.cpp: Near-Native GPU Speed in macOS Containers
 tract - an efficient rust neural network inference engine
 Beyond TinyML: Balance inference accuracy and latency on MCUs
 Bringing up bare metal ExecuTorch on RISC-V
 WebNN and WebLLM on RISC-V: Closing the AI Acceleration Gap with RVV and Tenstorrent
 Single-source cross-platform GPU LLM inference with Slang and Rust
 Closing the Loop: A Self-Learning Compiler for AI Accelerators
 One GPU, Many Models: What Works and What Segfaults
 Adventures in Model Quantization
 Vulkan API for Machine Learning? Competing with CUDA and ROCm in llama.cpp
 Running tinygrad and ggml on microcontroller NPUs
 The Hidden Cost of Intelligence: The Energy Footprint of AI from Code to GPU Kernels
 Lowering the barrier of entrance in AI-native system development
 Supercharging LLM serving with Dynamo
 Taming the LLM Zoo with Docker Model Runner: Inference with OCI Artifacts, llama.cpp, and vLLM
 From Infrastructure to Production: A Year of Self-Hosted LLMs
 A practical introduction to the ET platform.
 Zero to matmul with the ET-SoC-1
 All in RISC-V, RISC-V All in AI: Solving Real AI Compute Challenges with DeepComputing & Tenstorrent
 Review of kernel and user-space Neural Processing Unit (NPU) chips support on Linux
 TT-Boltz: Drug Discovery on Tenstorrent Hardware

Read the Call for Papers at https://aifoundry.org/#fosdem.

Event Speakers Start End

Saturday
  Welcome to the AI Plumbers Devroom
 Roman Shaposhnik, Tanya Dadasheva 10:30 10:35
  Multimodal support in llama.cpp - Achievements and Future Directions
 Xuan-Son Nguyen 10:35 10:55
  API Remoting for llama.cpp: Near-Native GPU Speed in macOS Containers
 Kevin Pouget 11:00 11:20
  tract - an efficient rust neural network inference engine
 Julien Balian, Mathieu Poumeyrol 11:25 11:45
  Beyond TinyML: Balance inference accuracy and latency on MCUs
 Charalampos Mainas, Anastassios Nanos 11:50 12:10
  Bringing up bare metal ExecuTorch on RISC-V
 William Jones, Jeremy Bennett, Shane Slattery, Pietra Ferreira 12:15 12:35
  WebNN and WebLLM on RISC-V: Closing the AI Acceleration Gap with RVV and Tenstorrent
 Yuning Liang, Petr Penzin 12:40 13:00
  Single-source cross-platform GPU LLM inference with Slang and Rust
 Crozet Sébastien 13:05 13:25
  Closing the Loop: A Self-Learning Compiler for AI Accelerators
 Ramon Wirsch 13:30 13:50
  One GPU, Many Models: What Works and What Segfaults
 YASH PANCHAL 13:55 14:15
  Adventures in Model Quantization
 ubergarm 14:20 14:40
  Vulkan API for Machine Learning? Competing with CUDA and ROCm in llama.cpp
 Ruben Ortlam 14:45 15:05
  Running tinygrad and ggml on microcontroller NPUs
 Roman Shaposhnik 15:10 15:15
  The Hidden Cost of Intelligence: The Energy Footprint of AI from Code to GPU Kernels
 Tushar Sharma 15:20 15:25
  Lowering the barrier of entrance in AI-native system development
 Tanya Dadasheva 15:30 15:35
  Supercharging LLM serving with Dynamo
 Piotr Tarasiewicz 15:40 16:00
  Taming the LLM Zoo with Docker Model Runner: Inference with OCI Artifacts, llama.cpp, and vLLM
 Eric Curtin, Dorin Geman 16:05 16:25
  From Infrastructure to Production: A Year of Self-Hosted LLMs
 Mateusz Charytoniuk, Gosia Zagajewska, Luiz Miguel 16:30 16:50
  A practical introduction to the ET platform.
 Gianluca Guida 16:55 17:20
  Zero to matmul with the ET-SoC-1
 Peter Cawley 17:25 17:45
  All in RISC-V, RISC-V All in AI: Solving Real AI Compute Challenges with DeepComputing & Tenstorrent
 Martin Chang, Danfeng Zhang 17:50 18:10
  Review of kernel and user-space Neural Processing Unit (NPU) chips support on Linux
 Jakov Petrina Trnski 18:15 18:35
  TT-Boltz: Drug Discovery on Tenstorrent Hardware
 Moritz Thüning 18:40 19:00