liberate-fhe
TransformerEngine
liberate-fhe | TransformerEngine | |
---|---|---|
1 | 2 | |
96 | 1,450 | |
- | 6.4% | |
8.0 | 9.5 | |
3 months ago | 5 days ago | |
Python | Python | |
BSD 3-clause Clear License | Apache License 2.0 |
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liberate-fhe
TransformerEngine
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Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1)
4090 now has its 8-bit float enabled as well, see the [transformer engine issue](https://github.com/NVIDIA/TransformerEngine/issues/15)
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GPUs for Deep Learning in 2023 – An In-depth Analysis
Would be curious to see your benchmarks. Btw, Nvidia will be providing support for fp8 in a future release of CUDA - https://github.com/NVIDIA/TransformerEngine/issues/15
I think TMA may not matter as much for consumer cards given the disproportionate amount of fp32 / int32 compute that they have.
Would be interesting to see how close to theoretical folks are able to get once CUDA support comes through.
What are some alternatives?
concrete-numpy - Concrete-Numpy: A library to turn programs into their homomorphic equivalent.
Whisper - High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model
openfhe-development - This is the development repository for the OpenFHE library. The current (stable) version is v1.1.4 (released on March 8, 2024).
autocvd - Tool to automatically set CUDA_VISIBLE_DEVICES based on GPU utilization. Usable from command line and code.
warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
ivy - The Unified AI Framework
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
fastaudio - 🔊 Audio and fastai v2
FastFold - Optimizing AlphaFold Training and Inference on GPU Clusters
PyTorch-Guide - PyTorch Guide
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration