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wtf is transformers-benchmarks?

mli/transformers-benchmarks — explained in plain English

Analysis updated 2026-07-17 · repo last pushed 2024-01-09

★ 912Jupyter NotebookAudience · researcherComplexity · 3/5DormantSetup · moderate

TL;DR

A collection of Jupyter notebooks that benchmark real training speed for Transformer models like BERT and GPT-2 across GPUs, so you can compare hardware cost versus actual performance.

Mindmap

mindmap
  root((repo))
    What it does
      Measures real GPU speed
      Reports TFLOPS achieved
      Compares GPU price vs speed
    Tech stack
      Jupyter Notebook
      PyTorch
      CUDA
    Use cases
      Choose GPU for training
      Estimate cloud costs
      Compare hardware options
    Audience
      ML researchers
      Startups planning compute

Code map

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Why would anyone build with this?

REASON 1

Decide which GPU to buy by comparing real measured training throughput against price.

REASON 2

Estimate cloud compute costs before training a custom BERT or GPT-2 model.

REASON 3

Benchmark individual Transformer layers to isolate performance bottlenecks on your own hardware.

What's in the stack?

Jupyter NotebookPyTorchCUDA

How it stacks up

mli/transformers-benchmarkskrishnaik06/interview-prepartion-data-sciencellsourcell/how-to-predict-stock-prices-easily-demo
Stars9121,041771
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Last pushed2024-01-092024-01-122022-06-23
MaintenanceDormantDormantDormant
Setup difficultymoderateeasymoderate
Complexity3/51/52/5
Audienceresearcherdatavibe coder

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · moderate Time to first run · 1h+

Requires a CUDA-capable GPU and running the provided Jupyter notebooks to get hardware-specific numbers.

Yoink these prompts

Prompt 1
Help me run these Transformer benchmark notebooks on my own GPU and interpret the TFLOPS results.
Prompt 2
Explain how to compare GPU cost-per-TFLOP using the data in this benchmark repo.
Prompt 3
Show me how to adapt the layer-level benchmark notebook to test a different model architecture.
Prompt 4
What batch size and CUDA version should I use to reproduce these BERT and GPT-2 training benchmarks?

Frequently asked questions

wtf is transformers-benchmarks?

A collection of Jupyter notebooks that benchmark real training speed for Transformer models like BERT and GPT-2 across GPUs, so you can compare hardware cost versus actual performance.

What language is transformers-benchmarks written in?

Mainly Jupyter Notebook. The stack also includes Jupyter Notebook, PyTorch, CUDA.

Is transformers-benchmarks actively maintained?

Dormant — no commits in 2+ years (last push 2024-01-09).

How hard is transformers-benchmarks to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is transformers-benchmarks for?

Mainly researcher.

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