| --- |
| license: apache-2.0 |
| pretty_name: Measured GPU microbenchmarks for transformer training |
| tags: [benchmark, gpu, performance, roofline, transformers, training] |
| configs: |
| - config_name: summary |
| data_files: "summary.csv" |
| - config_name: matmul |
| data_files: "data/*/matmul.csv" |
| - config_name: bandwidth |
| data_files: "data/*/bandwidth.csv" |
| - config_name: train_step |
| data_files: "data/*/train_step.csv" |
| --- |
| |
| # Measured GPU microbenchmarks for transformer training |
|
|
| Measured numbers for the GPUs most people can actually get on Colab and Kaggle: |
| matmul throughput across sizes, memory bandwidth, kernel launch overhead, and GPT-2-small |
| (124M) training-step time, each recorded with the GPU clock, power and temperature during the run. |
|
|
| ## Summary |
|
|
| | GPU | Platform | fp16 burst TFLOP/s | fp16 model-shape TFLOP/s | fp32 TFLOP/s | Copy GB/s | Launch µs | Step ms | Tokens/s | Noise % | Clock MHz (step) | Power W (step) | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| | Tesla T4 | colab | 40.18 | 23.09 | 4.16 | 232.9 | 8.15 | 516.55 | 15866 | 3.98 | 788 | 66.7 | |
| | NVIDIA L4 | colab | 75.1 | 49.06 | 15.9 | 229.9 | 8.67 | 283.11 | 28938 | 1.44 | 991 | 72.0 | |
| | NVIDIA RTX PRO 6000 Blackwell Server Edition | colab | 427.06 | 276.96 | 77.1 | 1459.1 | 3.32 | 45.0 | 182054 | 0.04 | 2338 | 504.2 | |
|
|
| Baseline step: GPT-2-small (124M), context 1024, batch 8, fp16 autocast, AdamW. |
| "fp16 model shapes" is the median throughput over the four matmul shapes that step actually runs. |
|
|
| ## Method |
|
|
| - Device time from CUDA events; median of 20–50 iterations after warm-up. |
| - Before each training-step measurement, 10 s of matmuls bring the GPU to its steady-state clock. |
| - Clock, power and temperature are sampled every 0.5 s *during* the timed run (reading them |
| afterwards is wrong: an idle GPU boosts instantly). |
| - fp32 means real fp32: TF32 is disabled. bf16 is excluded on GPUs without native bf16. |
| - FLOPs for the training step use 6 × params × tokens plus attention, a rule of thumb. |
|
|
| ## Files |
|
|
| `data/<gpu>_<platform>/`: `matmul.csv`, `bandwidth.csv`, `train_step.csv`, `hardware.json` |
| (the fact sheet), and per-GPU figures. `summary.csv` has one row per GPU. `figures/` has |
| cross-GPU charts. |
|
|
| ## Caveats |
|
|
| - One chip per GPU type and one session each. Chips of the same model can differ |
| (two T4s here ran ~10% apart in clock at the same power). |
| - Power-capped GPUs (T4, L4) vary 3–4% run to run with temperature; compare runs measured |
| back to back, as ratios. |
| - Small buffers in the bandwidth sweep can fit in L2 cache and read far above DRAM bandwidth. |
| - Launch overhead is timed from Python, so it depends on the host CPU as well as the GPU. |
| - A second T4 session measured 34.7 µs launch overhead, likely host-CPU contention; the table uses the first session's 8.15 µs (Kaggle T4: 7.85 µs). |
|
|
| ## Contribute a GPU |
|
|
| Run the notebook from the accompanying repository on any NVIDIA GPU and open a |
| pull request on this dataset with your results folder. |
|
|
