Vikram @msharmavikram
@NVIDIA Sr. Research Scientist Large Scale AI/ML Systems | Ph.D. (UIUC - Prof Wen-mei Hwu) All opinions and tweets are personal. msharmavikram.github.io Joined November 2008-
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For those who missed the talk and want to learn more about how to use CUDA C++ to implement custom CUDA kernels training GPT-like models, watch this!
For those who missed the talk and want to learn more about how to use CUDA C++ to implement custom CUDA kernels training GPT-like models, watch this!
Cannot wait for today talk by Jake and @g_evtushenko !
Cannot wait for today talk by Jake and @g_evtushenko !
Nom nom. Amazing!
I believe this is one of the critical discussions of our time if we truly want to address diversity and inclusion in AI/ML area. Expecting under-represented community members to have access and capabilities to publish and yet be part of top schools or research labs is impossible
I believe this is one of the critical discussions of our time if we truly want to address diversity and inclusion in AI/ML area. Expecting under-represented community members to have access and capabilities to publish and yet be part of top schools or research labs is impossible
Well written article from @dylan522p and team. Covers some of opinions that I have shared privately with a few stakeholders. Clearly, CXL if they need to succeed they must significantly upgrade!
Well written article from @dylan522p and team. Covers some of opinions that I have shared privately with a few stakeholders. Clearly, CXL if they need to succeed they must significantly upgrade!
"Inference is an install base problem."
"Inference is an install base problem."
I was searching for a few apartments and guess what? GenerativeAI at best!
This is so true. Don't get me wrong, these days 30 under 30 or 40 under 40 mean nothing to me. Mainly because it does not matter as the real work is done by someone unrecognized.
This is so true. Don't get me wrong, these days 30 under 30 or 40 under 40 mean nothing to me. Mainly because it does not matter as the real work is done by someone unrecognized.
Legal immigrants includes Scientists (NASA, NSF, and others), extraordinary skilled workers who are entrepreneurs, innovators contributing billions of dollars to US economy each year. @elonmusk, Surprisingly many senators, congressmen from both patrties are ignorant of this!!
Legal immigrants includes Scientists (NASA, NSF, and others), extraordinary skilled workers who are entrepreneurs, innovators contributing billions of dollars to US economy each year. @elonmusk, Surprisingly many senators, congressmen from both patrties are ignorant of this!!
Prof @AndrewYNg course was my introduction to the ML world. Although I barely understood some topics back then but in retrospect the material led solid foundation based on first principles.
Prof @AndrewYNg course was my introduction to the ML world. Although I barely understood some topics back then but in retrospect the material led solid foundation based on first principles.
This piece of advice by @sama is crux of creating great team and great products. Often people don't appreciate the importance of culture and concentrate too much on technology marvel or performance.
This piece of advice by @sama is crux of creating great team and great products. Often people don't appreciate the importance of culture and concentrate too much on technology marvel or performance.
I have worked with @socrates1024, and he is a fantastic collaborator, a great researcher, and a wonderful advisor (apart from being one of the smartest people that I have had the pleasure of working with). Please apply!
I have worked with @socrates1024, and he is a fantastic collaborator, a great researcher, and a wonderful advisor (apart from being one of the smartest people that I have had the pleasure of working with). Please apply!
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The Aarthi and Sriram.. @aarthisrirampod
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lmsys.org @lmsysorg
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Bill Dally @BillDally
681 Followers 0 Following Chief Scientist and Senior Vice President of Research at NVIDIA. Former Chair of Computer Science at Stanford University. Former Professor of CS at MIT.@msharmavikram Not a crazy amount in compute costs - but so much in "human" costs. Could focus on improving the data mix, trying out more initial learning rates, trying out different learning rate schedules etc in a single night (instead of over many many days)!
🔥llm.c update: Our single file of 2,000 ~clean lines of C/CUDA code now trains GPT-2 (124M) on GPU at speeds ~matching PyTorch (fp32, no flash attention) github.com/karpathy/llm.c… On my A100 I'm seeing 78ms/iter for llm.c and 80ms/iter for PyTorch. Keeping in mind this is fp32,…
Have you ever wanted to train LLMs in pure C without 245MB of PyTorch and 107MB of cPython? No? Well now you can! With llm.c: github.com/karpathy/llm.c To start, implements GPT-2 training on CPU/fp32 in only ~1,000 lines of clean code. It compiles and runs instantly, and exactly…
@msharmavikram @akankshanc @rzsgrt @CohereForAI @jeremyphoward Really appreciate the offer!! We do have a private discord channel in C4AI for the purpose of discussion and since the cohort has started and materials have been posted I'm not sure how smooth the migration would be. Although we can inform the members about cuda mode discord so…
thanks to @JeffDean and @SingularMattrix for their great leadership today; and @fchollet @dwarak and many others at @GoogleDeepMind for quickly charting a good and aligned path forward together. We can go back focusing on the unlimited amounts of good work ahead of us. (Jeff,…
Is Cosine-Similarity of Embeddings Really About Similarity? Netflix cautions against blindly using cosine similarity as a measure of semantic similarity between learned embeddings, as it can yield arbitrary and meaningless results. 📝arxiv.org/abs/2403.05440
@msharmavikram The probability distribution will contain the prob of all possible tokens, so you can always see the prob of the correct next token from the text being compressed. If the model hallucinates, it will have lower probability for the correct next token, therefore poor compression.
@msharmavikram Lossless, using arithmetic coding for compression. At every step they know the actual next token and they have the probability dist for the next token from llm. They feed this dist to the coder to compress. No hallucination because they know the actual next token
I'm thrilled to see my CUDA explainers already making a difference. The gang at @Mobius_Labs have just sped up their already-awesome HQQ dequantization by using CUDA -- and were kind enough to credit my tutorial! ❤️ As @karpathy said: accessibility matters.
Yes. Developers on PC GPUs are the key enablers to DC GPU success. So all the dev tools need to work flawlessly on PC GPUs. Currently this is largely true with Geforce. Radeons definitely got better these past 6 months and they are showing increased commitment to PC developer .…
@RajaXg I think AMD have screwed up in their inconsistent approach across consumer and data center cards, different feature sets limit developers to using cards that are harder to source. Production MI300 with devs using 7900XTX would make AMD much more practical.
If you liked my "Getting Started With CUDA for Python Programmers" video, then you might want to tune in this weekend for the sequel. I'll cover writing tiled kernels that use shared memory and thread sync to take advantage of the full speed of your GPU discord.gg/cudamode?event…
@msharmavikram @GoogleColab @neurosp1ke @WenmeiHwu That would be amazing - just sent you a DM.
My mom, at 60 years of age, just graduated with a Masters in Computer Science at the University of Illinois, Urbana-Champaign (with stellar grades too)! She doesn't believe you can be too old to be curious, which is a core value for me.
@msharmavikram Thank you, Vikram! I don't think that article would have turned out the way it did without your contributions. And, I think my writing improved in that process :) Happy new year!
I worked at Intel on Larrabee applications in 2007. Then I went to NVIDIA to work on ML in 2008. So I was there at both places at that time and I can say: NVIDIA's dominance didn't come from luck. It came from vision and execution. Which Intel lacked.
@msharmavikram @OpenAI There's a lot of things you can do with embeddings (clustering, classification, etc) but retrieval is the main thing we anticipated use for. You would use ada-002 to embed your query and then rank vectors using semantic similarity (cosine similarity). We also include BM25 sparse…
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