DeepSpeed VS TensorRT

Compare DeepSpeed vs TensorRT and see what are their differences.


DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. (by microsoft)


NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. (by NVIDIA)
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DeepSpeed TensorRT
51 22
32,550 9,065
3.2% 4.0%
9.8 5.0
3 days ago 10 days ago
Python C++
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.


Posts with mentions or reviews of DeepSpeed. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-06.


Posts with mentions or reviews of TensorRT. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-26.
  • AMD MI300X 30% higher performance than Nvidia H100, even with optimized stack
    1 project | | 17 Dec 2023
    > It's not rocket science to implement matrix multiplication in any GPU.

    You're right, it's harder. Saying this as someone who's done more work on the former than the latter. (I have, with a team, built a rocket engine. And not your school or backyard project size, but nozzle bigger than your face kind. I've also written CUDA kernels and boy is there a big learning curve to the latter that you gotta fundamentally rethink how you view a problem. It's unquestionable why CUDA devs are paid so much. Really it's only questionable why they aren't paid more)

    I know it is easy to think this problem is easy, it really looks that way. But there's an incredible amount of optimization that goes into all of this and that's what's really hard. You aren't going to get away with just N for loops for a tensor rank N. You got to chop the data up, be intelligent about it, manage memory, how you load memory, handle many data types, take into consideration different results for different FMA operations, and a whole lot more. There's a whole lot of non-obvious things that result in high optimization (maybe obvious __after__ the fact, but that's not truthfully "obvious"). The thing is, the space is so well researched and implemented that you can't get away with naive implementations, you have to be on the bleeding edge.

    Then you have to do that and make it reasonably usable for the programmer too, abstracting away all of that. Cuda also has a huge head start and momentum is not a force to be reckoned with (pun intended).

    Look at TensorRT[0]. The software isn't even complete and it still isn't going to cover all neural networks on all GPUs. I've had stuff work on a V100 and H100 but not an A100, then later get fixed. They even have the "Apple Advantage" in that they have control of the hardware. I'm not certain AMD will have the same advantage. We talk a lot about the difficulties of being first mover, but I think we can also recognize that momentum is an advantage of being first mover. And it isn't one to scoff at.


  • Getting SDXL-turbo running with tensorRT
    1 project | /r/StableDiffusion | 6 Dec 2023
    (python "a beautiful photograph of Mt. Fuji during cherry blossom").
  • Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
    14 projects | | 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 | | 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 | | 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,, 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:
    3 projects | /r/StableDiffusion | 21 Feb 2023
    It uses the nvidia demo code from:
  • [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: - 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.
  • demoDiffusion on TensorRT - supports 3090, 4090, and A100
    1 project | /r/StableDiffusion | 10 Dec 2022

What are some alternatives?

When comparing DeepSpeed and TensorRT you can also consider the following projects:

ColossalAI - Making large AI models cheaper, faster and more accessible

FasterTransformer - Transformer related optimization, including BERT, GPT

Megatron-LM - Ongoing research training transformer models at scale

onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX

fairscale - PyTorch extensions for high performance and large scale training.

vllm - A high-throughput and memory-efficient inference and serving engine for LLMs

accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support

openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

stable-diffusion-webui - Stable Diffusion web UI

mesh-transformer-jax - Model parallel transformers in JAX and Haiku

flash-attention - Fast and memory-efficient exact attention