Graphcore Research

Our mission is to follow and contribute to the advancement of AI research, aiming to characterise the computational requirements of machine intelligence.

[Graphcore Research @GCResearchTeam]

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 addressed with one simple fix 🎲

Check it out in the link below! 👇

[Graphcore Research @GCResearchTeam]

Check out the blog 👉 https://t.co/fgxRVXyZhD

[Graphcore Research @GCResearchTeam]

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 addressed with one simple fix 🎲

Check it out in the link below! 👇

[Graphcore Research @GCResearchTeam]

Link to blog ➡️ https://t.co/9rse2V6ZZP
Link to paper ➡️ https://t.co/APIZOktYMK

[Graphcore Research @GCResearchTeam]

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 can outperform 4-bit and 16-bit weights on a fixed weight memory budget.

In our work
- ⚖️ A scaling laws evaluation prompts us to consider very low-bit formats
- 📈 Scaled-up tests show the power of memory-matched models with 1-bit weights
- ⚡ Kernel benchmarking demonstrates their feasibility for autoregressive inference

Read all about it in our blog and paper (link below! ⬇️)

Massive thanks to our collaborators at Aleph Alpha Research!

Authors: @SohirMaskey, Constantin Eichenberg, @atomicflndr and @douglasahorr

[Graphcore Research @GCResearchTeam]

✨ Check out our summaries and follow our blog ✨https://t.co/cYtlcxuife

[Graphcore Research @GCResearchTeam]

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.

🌫️ In Intelligence per Watt, the authors discuss our favorite metric on large language models: energy efficiency. And how to take advantage of edge AI inference.

🕸️ Finally, Int vs FP: A comprehensive study of fine-grained formats is contributing to an old-timer topic in quantization: integer vs floating (block) point formats.

Check out our summaries 👇

[Graphcore Research @GCResearchTeam]

Come to the Frontiers in Probabilistic Inference today at #NeurIPS2025 to see Graphcore researcher Michael Pearce presenting "Variational Entropy Search is Just 1D Regression"

[Graphcore Research @GCResearchTeam]

Come to the ML for Systems workshop at #NeurIPS2025 to see Graphcore Researcher Callum McLean presenting "MXNorm: Reusing Block Scales for Efficient Tensor Normalisation"

[Graphcore Research @GCResearchTeam]

At #NeurIPS in San Diego? Come swing by poster #1016 this afternoon to see Graphcore Researcher Johanna Vielhaben presenting "Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers"

[Graphcore Research @GCResearchTeam]

✨Paper✨ https://t.co/3usCopvQlb
✨Blog post✨ https://t.co/Id2LJ8YWKF

[Graphcore Research @GCResearchTeam]

How does the structure of a Knowledge Graph influence model accuracy in #DrugDiscovery?

Our comprehensive study with @AstraZeneca on the effects of graph topology on Knowledge Graph Completion models has just been published in Bioinformatics!

Learn more in the paper and the blog post below! 👇

[Graphcore Research @GCResearchTeam]

Apply here: https://t.co/nXYYRHEfCV

[Graphcore Research @GCResearchTeam]

🚨 Graphcore is hiring AI Research Interns! 🚨

Join us to work at the intersection of hardware and AI and help shape the future of AI systems. Whether you're excited about efficient inference, large-scale training, or advancing frontier-model capabilities, we’ve got cutting-edge projects for you to dive into.

Interested? Apply below 👇

[Graphcore Research @GCResearchTeam]

✨ Check out our summaries and follow our blog ✨ https://t.co/FjJCsREX4m

[Graphcore Research @GCResearchTeam]

Our picks for October’s Papers of the Month are here. Out of 49 shortlisted papers, we spotlight 4 that stand out for their clever ideas on making #LLMs faster, smarter, and more efficient!

📊 First up, Grouped Lattice Vector Quantisation introduces a novel technique for a fine-grained post-training quantisation of LLMs, retaining good performance even at low bit widths.

🌫️ In Planned Diffusion, @danielmisrael and colleagues combine autoregressive and diffusion models. While the autoregressive model creates a scaffold and plan, the diffusion model fills the gaps, achieving extremely low-latency text generation.

🤔 Is your LLM overthinking it? Rethinking Thinking addresses the problem of lengthy reasoning chains by bounding their thinking space and gradually distilling their thoughts, speeding up reasoning without losing depth.

🕸️ Finally, When Structure Doesn’t Help compares techniques for how LLMs read text attributed graphs. The results are rather surprising: sometimes, too much structure can hurt.

Check out our summaries 👇

[Graphcore Research @GCResearchTeam]

Learn more by signing up here! https://t.co/h57cWP0KL3

[Graphcore Research @GCResearchTeam]

LLM using too many reasoning tokens? 😕
Generation slow? 🐌
Or simply too many steps before EOS? 🪜🪜🪜

Douglas Orr (@douglasahorr), our beloved research scientist, has got you covered! He will tell you the remedies to all of the above in the shortest time possible. Registration link in the 🧵 below!

(Special thanks to @CodeWordsAI and @join_ef)

[Graphcore Research @GCResearchTeam]

✨ https://t.co/USO1ctukVr ✨

[Graphcore Research @GCResearchTeam]

September's Papers of the Month is here, and this month is all about LLMs! 🧠

Out of all papers released this month, our editor @robhu92 has curated:

📊 "FlowRL: Matching Reward Distributions for LLM Reasoning“ (review by @samot_gc): A clever usage of #GFlowNets to align an #RL policy model with the *full* reward distribution, encouraging mode coverage

☁️ "Soft Tokens, Hard Truths" (review by @lukehudlass): A simple and scalable method to integrate #continuousthoughts with RL training schemes

🏎️ "Set Block Decoding is a Language Model Inference Accelerator" (review by @douglasahorr): Exactly what it says on the 🥫!

🔁 "Turning Recurring LLM Reasoning into Concise Behaviors" (review by @DobrikG): An improvement to #CoT reasoning, trying to reduce self-repetition via metacognitive reduce

Check out our reviews in the 🧵 below!

[Graphcore Research @GCResearchTeam]

Finally, Graph-R1 is another addition to the stack of agentic RAG approaches, but this time, using knowledge hypergraphs!

Summary: https://t.co/eB2TpCz6qf

[Graphcore Research @GCResearchTeam]

Summer may be over, but Papers of the Month certainly isn’t!

For August’s edition, we covered the following papers:

➡️ ADMIRE-BayesOpt
➡️ Guiding Diffusion Models with RL for Stable Molecule Generation
➡️ Graph-R1

🧵

[Graphcore Research @GCResearchTeam]

Next, Guiding Diffusion Models with RL for Stable Molecule Generation introduces reinforcement learning with physical feedback to accomplish exactly as its name suggests!

Summary: https://t.co/qbQsogSYPI