license: cc-by-4.0
pretty_name: SLMTrainBench
size_categories:
- 1K<n<10K
tags:
- benchmark
- gpu
- hardware
- pytorch
- openlanguagemodel
- small-language-model
- training
- mfu
- tabular
configs:
- config_name: default
data_files:
- split: benchmark
path: benchmark_catalog.csv
SLMTrainBench
SLMTrainBench is the measurement dataset for When Peak Floating-Point Throughput Misleads: Utilization and Cost Frontiers for Small Language Model Pretraining. It maps batch-saturated, single-GPU training performance for nine dense decoder-only models from 150 million to 8 billion parameters across ten NVIDIA GPUs and context lengths from 512 to 32,768 tokens.
The dataset contains 2,963 tested batch configurations, including successful measurements and out-of-memory boundaries. It reports tokens per second (TPS), model floating-point operations utilization (MFU), peak allocated and reserved video memory (VRAM), timing stability, attention backend, seed, and provenance.
What was measured
Each timed step performs:
- AdamW gradient/state zeroing;
- BF16-autocast forward propagation;
- shifted-token cross-entropy;
- backward propagation; and
- an AdamW optimizer update with FP32 parameters, gradients, and optimizer state.
The runs use eager PyTorch 2.8.0 and OpenLanguageModel (OLM), with
torch.compile, activation checkpointing, and gradient accumulation disabled.
Synthetic token tensors remain on the GPU. These are therefore steady-state
model-step measurements, not end-to-end dataloader throughput, total training
cost, or time-to-quality measurements.
For every GPU, model, and context combination, the harness tests power-of-two batch sizes and records 20 timed steps after adaptive warmup. Seeds 11 and 22 change model initialization and token values; they are timing replicates, not independent machines or training-quality trials.
Coverage
| Dimension | Values |
|---|---|
| GPUs | A100 80GB PCIe, B200, B300 SXM6 AC, RTX 4090, RTX 5090, H100 80GB HBM3, H200, RTX 6000 Ada, RTX A6000, RTX PRO 6000 Blackwell Server Edition |
| Model labels | 150M, 250M, 350M, 500M, 700M, 1B, 2B, 4B, 8B |
| Context lengths | 512, 1,024, 2,048, 4,096, 8,192, 16,384, 32,768 |
| Seeds | 11, 22 |
| Precision | BF16 autocast compute; FP32 parameters, gradients, and Adam states |
| Software | PyTorch 2.8.0, CUDA 12.8, Python 3.12.3, OLM eager mode |
The grid is intentionally ragged: long contexts and large models are present only when they fit, and the batch sweep stops at an out-of-memory event or the protocol's saturation rule.
Files
benchmark_catalog.csv: viewer-friendly flat table containing all 2,963 tested configurations.benchmark-data.json: canonical nested release artifact, including protocol, model, GPU, pricing, provenance, row, and interpolation metadata.metadata/provider_pricing_snapshot.json: 43 dated provider quotes from RunPod, Vast.ai, Lambda, Amazon Web Services, and Google Cloud.metadata/runpod_pricing_snapshot.json: earlier RunPod-only price input kept as a historical audit record.metadata/rtx4090_metadata_erratum.json: source-URL-only correction; no performance measurement changed.
Provider prices were captured on 27 July 2026 and are historical observations. Only performance on RunPod-hosted machines was measured. Costs for other providers transfer their dated hourly prices onto RunPod-measured TPS and are projections, not provider-specific benchmarks.
Loading
With Hugging Face Datasets:
from datasets import load_dataset
benchmark = load_dataset("FAIRC/SLMTrainBench", split="benchmark")
With pandas:
import pandas as pd
catalog = pd.read_csv(
"https://huggingface.co/datasets/FAIRC/SLMTrainBench/resolve/main/benchmark_catalog.csv"
)
The CSV is the only file configured for automatic loading. Download
benchmark-data.json directly when the full nested protocol and interpolation
metadata are needed.
CSV schema
| Column | Description |
|---|---|
source_file |
Provenance filename in the original benchmark archive. |
source_kind |
context_frontier measurement or reused context-2,048 baseline. |
gpu_name |
Captured NVIDIA device name. |
gpu_uuid |
Device UUID used to distinguish physical boards; not a credential. |
seed |
Input/model-initialization seed (11 or 22). |
model_key, model_label |
Machine- and human-readable model-size labels. |
actual_unique_parameters |
Exact number of unique trainable parameters. |
sequence_length |
Tokens per sequence. |
batch_size |
Sequences per optimizer step. |
tokens_per_step |
batch_size * sequence_length for completed rows. |
status |
complete, oom, or oom_during_model_build. |
stable |
Whether adaptive warmup passed; absent for failed rows. |
warmup_steps_executed |
Warmup steps before retained timing began. |
measured_steps |
Number of retained timed steps. |
median_step_time_ms |
Median complete-step time in milliseconds. |
mean_based_tokens_per_second |
Tokens divided by arithmetic-mean step time. |
measured_robust_relative_jitter |
Median absolute deviation divided by median retained step time. |
tokens_per_second |
Primary TPS, computed from median step time. |
achieved_tflops |
Modeled training floating-point operations per second in TFLOP/s. |
dense_bf16_peak_tflops |
Nominal vendor dense-BF16 peak used as the primary MFU denominator. |
model_flops_utilization_pct |
Nominal-reference MFU percentage. |
configured_clock_dense_bf16_peak_tflops |
Peak linearly adjusted to the captured application clock. |
configured_clock_model_flops_utilization_pct |
Configured-clock MFU sensitivity value. |
peak_allocated_gb, peak_allocated_vram_pct |
Peak PyTorch-allocated VRAM. |
peak_reserved_gb, peak_reserved_vram_pct |
Peak PyTorch-reserved VRAM. |
selected_sdpa_backend |
PyTorch scaled dot-product attention backend when captured. |
error |
Failure text for out-of-memory rows. |
Missing values are expected for metrics that cannot be produced by an out-of-memory run. Two model-build failures also lack model- and batch-level fields.
Metric definitions
For batch size (b), sequence length (s), and median step time (t):
[ \mathrm{TPS}=\frac{bs}{t}. ]
For unique parameters (P), layers (n_l), hidden width (h), and context length (s), the benchmark models training work per token as
[ f_{\mathrm{token}} = 6P + 12n_lhs. ]
Achieved modeled throughput is (A=\mathrm{TPS},f_{\mathrm{token}}/10^{12}), and nominal-reference MFU is (100A/F_{\mathrm{BF16,nom}}). MFU is a modeled fraction of vendor peak, not a hardware-counter measurement; the FLOP model omits elementwise operations.
Intended use
Use SLMTrainBench to:
- compare measured single-GPU TPS, MFU, and memory use for this workload family;
- locate tested batch sizes and out-of-memory boundaries;
- reproduce the paper's batch-selection and cost-frontier analyses; and
- form planning hypotheses for nearby model sizes before validating the focal configuration on the intended machine.
Limitations
- Results cover one OLM Llama-style architecture, eager PyTorch, BF16 compute, and NVIDIA GPUs. They do not establish rankings for compiled graphs, FP8, alternate kernels, other frameworks, or other accelerators.
- Most GPU models were tested on one rented board; B300 used two boards. The two seeds do not measure host-to-host or provider-to-provider variance.
- Synthetic resident tokens exclude input pipelines, checkpointing, evaluation, networking, failures, and setup/idle time.
- This is a single-GPU benchmark. Do not estimate multi-GPU wall time by simply multiplying TPS; communication and scaling efficiency must be measured.
- Prices and marketplace availability change. Treat the supplied quotes only as dated, auditable snapshots.
- The released surrogate is validated only within the measured 150M-8B and context-512-32,768 region and should not replace a focal validation run.
Related resources
Citation
@misc{mankash2026peakthroughput,
title = {When Peak Floating-Point Throughput Misleads: Utilization and Cost Frontiers for Small Language Model Pretraining},
author = {Tavish Mankash and Vardhaman Kalloli and Keshava Prasad and Deepan Muthirayan},
year = {2026},
howpublished = {Preprint},
note = {SLMTrainBench dataset, version 1.0.0},
url = {https://huggingface.co/datasets/FAIRC/SLMTrainBench}
}
License
The measurement dataset and its metadata are released under the Creative Commons Attribution 4.0 International license (CC BY 4.0). Cite the accompanying preprint and identify SLMTrainBench version 1.0.0 when redistributing or adapting the data.