The Gemini era is here. Thrilled to launch Gemini 1.0, our most capable & general AI model. Built to be natively multimodal, it can understand many types of info. Efficient & flexible, it comes in 3 sizes each best-in-class & optimized for different uses https://t.co/VUu1277bC2
Introducing the AlgoPerf: Training Algorithms Benchmark! Compete for a share of the $50,000 prize pool by submitting more effective and efficient neural network training algorithms. Learn more https://t.co/csfXkUCKbN
#Algorithms#MachineLearning#Competition
SC23 attendees join MLCommons BOF sessions to add your voice to the @MLCommons community. Wed, 11/15, 5:15pm in Rm 601-603 MLPerf: A Benchmark for Machine Learning, or in Rm 702 join the conversation around the Future of Benchmarks in Supercomputing. https://t.co/xjuOrQqXjW #SC23
Today on the blog, learn how we’re supporting a new effort by the non-profit MLCommons Association that aims to bring together expert researchers across academia and industry to develop standard AI safety benchmarks that everyone can use and understand. ↓ https://t.co/stExJ386UD
Excited to announce a HUGE secret with @LisaSu: @LaminiAI has been building LLMs on @AMD GPUs *in production* for over the past year!
We’ve made running LLMs on AMD super easy and a highly competitive option through our LLM Superstation, available now at ~10x lower cost than cloud. 👉🏻 https://t.co/wqx9d8mPOK
Our enterprise customers have already built *thousands* of private LLMs on @LaminiAI LLM Superstations, e.g. @iFit leading at-home fitness with millions of users and @AMD itself:
🚀 Easy & fast: “It was simple to iterate and deploy with a few lines of code and amazingly fast with the AMD Instinct™ hardware.”
⭐️ LLMs are the new IP: “Using a public LLM wasn’t enough: we needed something that we could easily and quickly personalize to our customers’ data and constantly improve on new data, while keeping all of our data private.”
⚙️ Any infrastructure: “We’ve deployed Lamini in our internal Kubernetes cluster with AMD Instinct GPUs, and are using finetuning to create models trained on [our data].”
We had a cameo quote from Joe Spisak at @MetaAI who leads the Llama efforts said: “…Llama 2 is becoming the foundation of some of the most innovative companies.”
🫱🏼🫲🏾 Join Fortune 500 enterprises, and get your own private LLM Superstations—hosted, VPC, or on-premise (just 2 questions): https://t.co/ECVOsux9ay
More (technical) details here👉🏻 https://t.co/wqx9d8mPOK
@jabsNtriangles Lethal Weapon showed us what happens when someone knows jiu-jitsu (they hired Rorion Gracie to serve as a technical advisor)
https://t.co/jUHJZnCheb
@krismicinski My lab was able to do that for experimental architectures we were using (and there was a lot of red tape), but there were exclusive contracts in place keeping us from doing that for any of our clusters.
@HPC_Guru@AmpereComputing@AMD@ServeTheHome Time will tell, but I think Ampere cutting their system level cache in half to accommodate the higher core count (compared to Altra) is going to hurt their performance in the long run.
Let's talk about a detail that occurs during PyTorch 2.0's codegen - tiling.
In many cases, tiling is needed to generate efficient kernels. Even for something as basic as torch.add(A, B), you might need tiling to be efficient! But what is tiling? And when is it needed?
(1/13)
Delighted to share our new @GoogleHealth@GoogleAI @Deepmind paper at the intersection of LLMs + health.
Our LLMs building on Flan-PaLM reach SOTA on multiple medical question answering datasets including 67.6% on MedQA USMLE (+17% over prior work).
https://t.co/jZZuFDrxGw
Our paper "Benchmarking Graph Neural Networks" has been accepted for publication at Journal of Machine Learning Research @JmlrOrg!
https://t.co/OXq9uJwt9u
(after rejection from NeurIPS, ICLR and ICML :)