12.5.5 : AI

• Paulius Micikevicius : NVIDIA
• recording problem
• hope for replay
• Slides
• FP4 for inference
• MXFP8 : train on 8bits
• FP16 : factor for the hole gradien
• FP8 : scale factor for tensor
• FP4 : scale factor for block
• Need to block dot product for example and store scale in an efficency way (just for tensor cores)
• Successful training could need from 10 to 20 binades
• You don't want to round to the nearest but to the good range
• decode scale is rounded up and encode scale is rounded down
• OK to have few value to 0,
• FLUX in FP4 : quite similar
• PTQ : Post Training Quantization (converting a FP16 trained model into FP4)
• LLM FP4 PTQ : lost a bit of precision at inference
• QAT : Quantization Aware Training
• MXFP8 : easy path for 8-bits inference
• If you are compute limited, you could add sparsity of use a lower precision
• If you are bandwidth limited its depdends which tensor is limiting the speed.
• Addition are generally still in FP32 àr FP16 (which is totally normal even in smaller precision)
• Hopper : fast accumulator mode but lost accuracy
• FP4 quite decent accuracy
• Quantization operation is not differentiable
• Possibility to get back the score of BF16 with FP4
• He's comparing FP8 for H200 and B200 but there are 2 types of FP8. So which one is used ?
• Algorithm to find the layers which can be well compressed and still keep accuracy
• KV cache can use a huge part of the GPU memory so it is important to quantize it
• Using NVidia Nemotron 4
• SVDQuant decomposes GEMM layer into simpler part to keep accuracy and reduce size
• Distillation with Quantization
• Integer quantization could work with diffusion models but do not produce the best results
• Speculative decoding computes multiple token in one generation

• Pratyush Kumar : Co-Founder, Sarvam AI
• Sunil Gupta : Co-Founder, Managing Director, and Chief Executive Officer, Yotta Data Centres
• Slides
• This guy is talking super fast
• Tokenizer very efficient on Indan Languages (10 Languages + english)
• From 52 to 296 tok/s on H100 (with 32 streams)
• 20 cents per 100 token
• Text to speach for all indian languages

• Ce Zhang : CTO, Together AI
• Slides
• Very interesting presentation but it is better to have the slide because there are a lot of schemas
• why only 80% of hardware ?
• Attention is time consuming, => replace with Hyena (convolution)
• Speculative prefill : you can keep only 10% of the tokens as input
• A good model is a good speculator

• Yejin Choi : Senior Research Director, NVIDIA
• Slices
• The era of brute force scaling is over
• SuperBPE makes token bigger (first learn by subwords followed by superwords
• Less number of token to train over
• Me : from sylabic to words to expressions
• Socratic MCTS : Maieutic : questioning to knowledge
• the LLM generate qestions during the reasoning
• But verifier is very hard to do
• Between 1 to 9 minutes per questions (average score 0.748 when GPT4o (0513) is at 0.540 and Claude 3.5 sonnet : 0.550)
• Retro search for better reasoning
• Challenge the status quo about searches
• LLM can generate very long though and still when to the wrong answer (underthinking)
• sometimes short term reasoning is better
• Look at alternative path and see if they lead to a good answer
• Test time training for
• Unatural split between training and testing
• Relevant for video long content
• For a minute video, classic transformers cannot handle it because it would take 1000000 token for that
• Learning to learn at tested time (personnally, I though test was there to prevent overfitting, but why not)
• In this paradigm, hidden states are considered as part of the model
• On Tom and Jerry, this shapes likes Tom and Jerry but the animation and consideration are really far from orignal William Hanna and Joseph Barbera work.
• Even Tom and Jerry of Chuch Jones looks better...
• The art of new and diverse synthetic data generation
• Need a metric for diversity and Diverse topic does not matter for math
• mathematical reasoning

• Vartika Singh : Strategic AI Lead, NVIDIA
• Noam Brown : Research Scientist, OpenAI (2017, poker AI 2 players, 2019 : multiplayer poker, then extand to diplomacy, all techniques of Reasoning for Poker, Chess and diplomacy were different and the idea was to find a way to generalize them all
• Bryan Catanzaro : VP, Applied Deep Learning Research, NVIDIA. CUDNN, DLSS. Everyday I try to make researchers work together.
• How do we improve inference but not a priority at the time for poker (games with partial information)
• There wasn't a universal way to train on pocker, chess, diplomacy
• Pluribus for poker : 150$ to train a model at this time, on 28 CPU cores
• Diplomacy games can spread on several weeks or month
• The poker model was good so let's try on diplomacy
• Matrix multiplication is the best way to create a computer that can do a lot of work
• This is not trivial to trun matrix multiply into intelligence
• We have to develop a Reasoning technique which is as performant as deeplearning for images
• GPTo1 creates a new paradigm. As Alexnet several years ago.
• model performance with a value is not relevant anymore because the inference time is not taken account.
• Yes there are things that the models cannot do today but people are working on it

• Giuseppe Fiameni : Solutions Architect, NVIDIA
• Karin Sevegnani : Solutions Architect, NVIDIA
• Slides
• Both CPU and GPU super critical for computing tasks
• PCIe is a bottleneck for CPU GPU programming => Virtual Unified Memory for Grace Hopper SuperShip
• NeMo parallelise computation over 100 000s of nodes
• NeMo integrate into a NVidia Triton inference server
• Rocky Linux 9.3 with custom kernel
• LoRA : efficient fine tuning method for Llama
• nsys : -y to specify the time of the profiling and -d do delay when profiling starts
• Profiling show the program is compute bound and not memory bound
• wait causes by autograd of PyTorch
• Offloading : memory store on GPU during training as temporary moved to CPU to reduce the memory used by the GPU => it reduce GPU memory and GPU utilisation but more synchronisation between CPU and GPU.
• Mixed precision training to optimise training
• FP8 training : speed improvement and efficency on Hopper

• Jo Plaete : CIO and VFX Supervisor, Metaphysic, Metaphysic
• Ed Ulbrich : Chief Content Officer and President of Production, Metaphysic
• Very Insteresting but a lot of illustration, so hard to take notes
• Slides
• From 3D assets rendering to Neral Network inference
• Real time generative AI
• They take care of license for training