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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Why async gradient update doesn't get popular in LLM community?
3 projects | news.ycombinator.com | 10 Oct 2023
Why Did Google Brain Exist?
2 projects | news.ycombinator.com | 26 Apr 2023
GPU cluster scaling has come a long way. Just checkout the scaling plot here: https://github.com/NVIDIA/Megatron-LM
I asked ChatGPT to rate the intelligence level of current AI systems out there.
2 projects | /r/ChatGPT | 11 Mar 2023
Google's PaLM, Facebook's LLaMA, Nvidia's Megatron, I am missing some surely and Apple sure has something cooking as well but these are the big ones, of course none of them are publicly available, but research papers are reputable. All of the ones mentioned should beat GPT-3 although GPT-3.5 (chatGPT) should be bit better and ability to search (Bing) should level the playing field even further, but Google's PaLM with search functionality should be clearly ahead. This is why people are excited about GPT-4, GPT-3 was way ahead of anyone else when it came out but others were able to catch up since, we'll see if GPT-4 will be another bing jump among LLMs.
Nvidia Fiscal Q3 2022 Financial Result
4 projects | /r/nvidia | 17 Nov 2021
Described a collaboration involving NVIDIA Megatron-LM and Microsoft DeepSpeed to create an efficient, scalable, 3D parallel system capable of combining data, pipeline and tensor-slicing-based parallelism.
Microsoft and NVIDIA AI Introduces MT-NLG: The Largest and Most Powerful Monolithic Transformer Language NLP Model
2 projects | /r/LanguageTechnology | 13 Oct 2021
Microsoft and NVIDIA present the Megatron-Turing Natural Language Generation model (MT-NLG), powered by DeepSpeed and Megatron, the largest and robust monolithic transformer language model trained with 530 billion parameters.
[R] Data Movement Is All You Need: A Case Study on Optimizing Transformers
2 projects | /r/MachineLearning | 19 Jan 2021
Nvidia's own implementation of Transformers, i.e, Megatron on NVIDIA's Selene supercomputer (where GPT-3 is possible too) -https://github.com/NVIDIA/Megatron-LM
Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
14 projects | news.ycombinator.com | 26 Sep 2023
TVM and other compiler-based approaches seem to really perform really well and make supporting different backends really easy. A good friend who's been in this space for a while told me llama.cpp is sort of a "hand crafted" version of what these compilers could output, which I think speaks to the craftmanship Georgi and the ggml team have put into llama.cpp, but also the opportunity to "compile" versions of llama.cpp for other model architectures or platforms.
Nvidia Introduces TensorRT-LLM for Accelerating LLM Inference on H100/A100 GPUs
3 projects | news.ycombinator.com | 8 Sep 2023
Maybe? Looks like tensorRT does work, but I couldn't find much.
Train Your AI Model Once and Deploy on Any Cloud
3 projects | news.ycombinator.com | 8 Jul 2023
highly optimized transformer-based encoder and decoder component, supported on pytorch, tensorflow and triton
TensorRT, custom ml framework/ inference runtime from nvidia, https://developer.nvidia.com/tensorrt, but you have to port your models
A1111 just added support for TensorRT for webui as an extension!
5 projects | /r/StableDiffusion | 27 May 2023
WIP - TensorRT accelerated stable diffusion img2img from mobile camera over webrtc + whisper speech to text. Interdimensional cable is here! Code: https://github.com/venetanji/videosd
3 projects | /r/StableDiffusion | 21 Feb 2023
It uses the nvidia demo code from: https://github.com/NVIDIA/TensorRT/tree/main/demo/Diffusion
[P] Get 2x Faster Transcriptions with OpenAI Whisper Large on Kernl
7 projects | /r/MachineLearning | 8 Feb 2023
The traditional way to deploy a model is to export it to Onnx, then to TensorRT plan format. Each step requires its own tooling, its own mental model, and may raise some issues. The most annoying thing is that you need Microsoft or Nvidia support to get the best performances, and sometimes model support takes time. For instance, T5, a model released in 2019, is not yet correctly supported on TensorRT, in particular K/V cache is missing (soon it will be according to TensorRT maintainers, but I wrote the very same thing almost 1 year ago and then 4 months ago so… I don’t know).
Speeding up T5
2 projects | /r/LanguageTechnology | 22 Jan 2023
I've tried to speed it up with TensorRT and followed this example: https://github.com/NVIDIA/TensorRT/blob/main/demo/HuggingFace/notebooks/t5.ipynb - it does give considerable speedup for batch-size=1 but it does not work with bigger batch sizes, which is useless as I can simply increase the batch-size of HuggingFace model.
An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
10 projects | /r/learnmachinelearning | 26 Jul 2022
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
I was looking for some great quantization open-source libraries that could actually be applied in production (both edge or cloud CPU/GPU). Do you know if I am missing any good libraries?
4 projects | /r/learnmachinelearning | 14 Jul 2022
Nvidia Quantization | Quantization with TensorRT
Can you run a quantized model om GPU?
4 projects | /r/deeplearning | 25 Jun 2022
You might want to try Nvidia's quantization toolkit for pytorch: https://github.com/NVIDIA/TensorRT/tree/main/tools/pytorch-quantization
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
FasterTransformer - Transformer related optimization, including BERT, GPT
flash-attention - Fast and memory-efficient exact attention
stable-diffusion-webui - Stable Diffusion web UI
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
ColossalAI - Making large AI models cheaper, faster and more accessible
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀