Our mission is to follow and contribute to the advancement of AI research, aiming to characterise the computational requirements of machine intelligence.
11 billion parameters. On a phone.
We wanted to see whether Llama-3.2-11B-Vision-Instruct — far too large for a phone in its original form — could fit into a ~4 GB budget.
The answer: yes. Here's how 🧵
A tiny implementation detail in low-precision arithmetic could be biasing your AI training 😲
This interactive deep dive from Graphcore Research's @Awfidius uncovers a subtle failure mode in stochastic rounding that only appears when randomness is limited, and how it can be
Would you rather use 1 million × 16-bit weights, 4 million × 4-bit weights, or even 16 million × 1-bit weights?
In joint work between Aleph Alpha Research and Graphcore, we asked this question of LLMs — the answer encouraged us to embrace the wonder ✨ of 1-bit weights, which
Our picks for November’s Papers of the Month are here. Out of our shortlisted papers, we spotlight three looking at LLM efficiency from different angles!
📊 First up, How to Scale Second-Order Optimization is studying optimal tuning of second order optimizers such as Muon.
🌫️
Come to the Frontiers in Probabilistic Inference today at #NeurIPS2025 to see Graphcore researcher Michael Pearce presenting "Variational Entropy Search is Just 1D Regression"