August, even with its heat waves and holidays, left no shortage of exciting research. Our top papers for this month are the following: ADMIRE-BayesOpt tha...
As July brought tennis at Wimbledon, so too did the ML world serve up a volley of research. This month, we took an eagle-eyed approach—or, perhaps, Hawk Eyed...
This June not only brought us very hot and sunny days (at least here in the UK), but also an excellent selection of new and exciting ML research! Out of the ...
Your boss emails you a point in 128-billion-dimensional space. “Llama 3.1 8B,” the message reads. “A not-so-large language model in bfloat16. But it’s too bi...
Hurtling past the NeurIPS submission deadline into the summer months, we switch from huddling around server rooms to keep warm to babysitting experiments whi...
April has been a busy month for the AI research community, with ICLR (the first of the “big three” AI conferences of the year) taking place in Singapore. We’...
We’ve enjoyed March, bringing improving weather and many excellent ML papers to keep us busy. As usual, we’re here to share summaries of four of our favourit...
Welcome to Papers of the Month! This time around, our monthly selection of ML papers revolves around the central theme of scale – and learning how to scale e...
New year, new Papers of the Month! Kicking off 2025, it’s apparent that reasoning and test-time compute are the hot topics on the block, with much research i...
Welcome to Papers of the Month — Graphcore Research’s effort to bring you our pick of the most interesting ML papers. In December we noted a collection of pa...
Vision-Language Models (VLMs) allow LLMs to “see”, but how do they work? In this post, we’ll walk through the model changes needed to turn an LLM into a VLM ...
This month we’ve got an all-LLM menu of papers for you, with summaries of four great works exploring many different aspects of crafting systems for LLM train...
This month brought us some exciting developments in improving image-generating models, as well as some interesting insights into how to make large language m...
We are pleased to have announce we have open positions for Research Scientists and Engineers to join our team.
We’re pleased to share four papers from different domains: LLM self-correction, FP8 training, generative crystals and optimisation. They are united, somewhat...
With the rapid advances in the capabilities of large language models (LLMs), there is an increasing need for efficient inference platforms that would enable ...
If there’s one thing you can count on from Graphcore Research, it’s tireless enthusiasm for effective compute utilsation! Our favourite papers from August i...
My colleagues and I always get excited when, every once in a while, deep learning research throws up a fun little maths problem. Our recent work on u-μP does...
Scaling continues to be a super hot topic of research and our selection of papers for this month all tackle different angles of how to scale models efficient...
Improving transformers is now not “just one area” of machine learning research. This is illustrated by the breadth of papers we got excited about this month,...
May is always an eventful time of year for ML researchers, with final ICML paper decisions and ICLR taking place in early May, and NeurIPS submission deadlin...
For our April selection of AI research papers, there is a clear common thread: efficient LLM inference. But as it happens, ML researchers are showing there a...
Transformer-based LLMs seem mysterious, but they don’t need to. In this post, we’ll walk through a modern transformer LLM, Google’s Gemma, providing bare-bon...
March was a fruitful month for AI research, with plenty of papers for us to choose from. A trend in the work we’ve selected is the pushing of previously publ...
Improving LLM inference is a key research topic at the moment, and something we’re particularly interested in at Graphcore because of its hardware implicatio...
For the research community, 2023 was dominated by large transformers and the associated challenges with training, tuning and deploying them. This trend has c...
The last month saw impressive developments in the space of efficient transformers and applied ML, from materials discovery to chip design.
TL;DR: Scaled dot product attention isn’t properly scaled, and that’s a good thing!