gpt-fast
optimum-nvidia
| gpt-fast | optimum-nvidia | |
|---|---|---|
| 8 | 1 | |
| 5,152 | 785 | |
| 3.5% | 10.2% | |
| 8.3 | 9.3 | |
| 2 days ago | 4 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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gpt-fast
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[D] GPT-Fast performance on larger batch sizes
I'm toying around with gpt-fast (https://github.com/pytorch-labs/gpt-fast) and was wondering if anyone has run experiments @ BS>1?
- Optimum-NVIDIA - 28x faster inference in just 1 line of code !?
- GPT-Fast: Simple and efficient GPT inference in <1000 LOC of Python
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GPT-Fast: A fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!
And check out the code here: https://github.com/pytorch-labs/gpt-fast
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80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
How does this compare to PyTorch labs optimizations for Sam and llama2 ?
https://github.com/pytorch-labs/segment-anything-fast
https://github.com/pytorch-labs/gpt-fast
- Fast and hackable PyTorch native transformer inference
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Accelerating Generative AI with PyTorch II: GPT, Fast
I'm wondering if gpt-fast has a version that can be run from Windows Command Prompt or Powershell?
https://github.com/pytorch-labs/gpt-fast/issues/45
optimum-nvidia
What are some alternatives?
unsloth - Finetune Llama 3, Mistral & Gemma LLMs 2-5x faster with 80% less memory
TensorRT-LLM - TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
stable-fast - Best inference performance optimization framework for HuggingFace Diffusers on NVIDIA GPUs.
segment-anything-fast - A batched offline inference oriented version of segment-anything