datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gpu-prices
GPU Price Tracker
A continuously-updated dataset of cross-cloud GPU rental pricing
covering 13 public cloud providers (AWS, GCP, Azure, Lambda Labs,
RunPod, Vast.ai, DataCrunch, Cudo Compute, TensorDock, Vultr, Oracle,
Nebius, CloudRift): 3M+ listing observations, 70+ GPU types, collected
twice daily since January 2026 by scraping provider pricing surfaces via
the gpuhunt library and
published as Hive-partitioned Parquet files
(prices/dt=YYYY-MM-DD/*.parquet).
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/afhubbard/gpu-prices.backendbench_tests
TorchBench
The TorchBench suite of BackendBench is designed to mimic real-world use cases. It provides operators and inputs derived from 155 model traces found in TIMM (67), Hugging Face Transformers (45), and TorchBench (43). (These are also the models PyTorch developers use to validate performance.) You can view the origin of these traces by switching the subset in the dataset viewer to ops_traces_models and torchbench for the full dataset.
When running BackendBench, much of the… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/backendbench_tests.KernelBook
Overview
dataset_permissive{.json/.parquet} is a curated collection of pairs of pytorch programs and equivalent triton code (generated by torch inductor) which can be used to train models to translate pytorch code to triton code.
The triton code was generated using PyTorch 2.5.0 so for best results during evaluation / running the triton code we recommend using that version of pytorch.
Dataset Creation
The dataset was created through the following process:… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/KernelBook.triton-gpu-latency
Triton GPU Latency Dataset
A large dataset of PyTorch problems (mostly from KernelBench) paired with candidate Triton-kernel implementations and their measured GPU runtimes generated by MakoraGenerate. Each row is a self-contained Python program that defines (1) a reference Model written with plain PyTorch ops and (2) a ModelNew that re-implements the same forward pass with a hand-written or generated Triton kernel. The label is the runtime of executing ModelNew.
Built for… See the full description on the dataset page: https://huggingface.co/datasets/makora-ai/triton-gpu-latency.gpu-compatibility
Self-Hosted AI — GPU Compatibility, Recipes and Catalogue
Which open-weight AI models actually run on which consumer GPU, and what it takes
to get them running. 2 700 model×GPU verdicts across 100 models
and 27 cards, plus 1 009 full setup guides
(19 MB of markdown) written against specific hardware.
This is the machine-readable form of smeltcore.com. Every row carries a
url back to the page it came from.
Generated 2026-09-24T19:45:21+00:00 from the public read API… See the full description on the dataset page: https://huggingface.co/datasets/Smeltcore/gpu-compatibility.one-layer-deeper-submissions
One Layer Deeper submissions
This dataset archives 15,602 accepted uploads from 206 GitHub accounts to the One Layer Deeper competition. It contains 9,627 distinct source files, all upload metadata, and stored evaluation results. Snapshot: September 7, 2026, 21:48 UTC, after the August 31 submission deadline.
Split
Uploads
Succeeded
Failed
easy
11,961
11,112
849
medium
2,704
2,509
195
hard
937
847
90
All accepted uploads are included: practice runs, failures… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/one-layer-deeper-submissions.cuda-triton-gpu-kernels-2026
⚡ Complete 2026 CUDA & OpenAI Triton High-Performance GPU Kernel Engineering SFT/DPO Suite
The definitive, production-grade synthetic alignment dataset engineered for training and fine-tuning open-weights Large Language Models (Qwen 2.5 Coder, DeepSeek-Coder, Llama 3.1) on ultra-high-throughput GPU kernel programming: NVIDIA Hopper H100 / Blackwell B200 TMA async transfers, OpenAI Triton 3.1+ FlashAttention-3, 32-bank conflict elimination, and low-bit FP8 / INT4 GEMM… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/cuda-triton-gpu-kernels-2026.gpu-mode-triton-augment-sample
Dataset Card for gpu-mode-triton-augment-sample
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/winglian/gpu-mode-triton-augment-sample/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/winglian/gpu-mode-triton-augment-sample.gpu-forecasters-eval-setCompanion artifact for GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization. Code: codezakh/gpu-surrogates.
Held-out evaluation set used in the paper. Each row is one (reference, candidate) kernel pair on a GPU Mode task, with the candidate's measured speedup over the reference on an A100.
Loading
from datasets import load_dataset
# all six packs combined
ds = load_dataset("codezakh/gpu-forecasters-eval-set", name="combined", split="eval")
#… See the full description on the dataset page: https://huggingface.co/datasets/codezakh/gpu-forecasters-eval-set.gpu-forecasters-eval-set-predictionsCompanion artifact for GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization. Code: codezakh/gpu-surrogates.
Surrogate predictions on the held-out evaluation set. Each row is one forecast from one (surrogate, repeat) on one row of codezakh/gpu-forecasters-eval-set.
Loading
from datasets import load_dataset
# all surrogates and repeats
ds = load_dataset("codezakh/gpu-forecasters-eval-set-predictions", name="combined", split="predictions")
# one… See the full description on the dataset page: https://huggingface.co/datasets/codezakh/gpu-forecasters-eval-set-predictions.gpu-forecasters-puct-search-eventsCompanion artifact for GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization. Code: codezakh/gpu-surrogates.
Events emitted during the kernel searches in this work. You can reconstruct everything that happened in a search from these events: which kernels were tried, in what order, with what runtimes.
See the kernel-search code at codezakh/gpu-surrogates for the payload schema of each event kind.
Loading
from datasets import load_dataset
#… See the full description on the dataset page: https://huggingface.co/datasets/codezakh/gpu-forecasters-puct-search-events.ia-loaded-embedded-gpu
Dataset Card for "ia-loaded-embedded-gpu"
More Information needed
MixSub-LLaMA-3.2-Entities-Overlap-GPU-Scorecategorized_triton_data_permissive
Dataset Description
This dataset contains code snippets from Triton-based projects across GitHub, specifically filtered to include only repositories with permissive licenses (MIT, Apache, BSD, etc.). Each entry in the dataset includes:
Triton code snippet
Repository information
File path
Commit hash
Direct GitHub URL to the source code
License information
Categorization of the code functionality
Dataset Creation
The dataset was created by:
Collecting Triton code… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/categorized_triton_data_permissive.autonomous-cloud-gpu-slurm-serving-suite
⚡ Autonomous Cloud GPU Infrastructure, Slurm Orchestration & Distributed Serving Suite (2026)
A Production-Grade, Verifiable Synthetic Corpus for Training Autonomous AI Supercomputing & LLM Serving Agents
⚡ Overview & Industry Problem
Operating massive AI supercomputers (thousands of NVIDIA H100/H200 and Blackwell GPUs) requires coordinating Slurm cluster schedules, topology-aware NVLink cliques, NCCL AllReduce rings, RoCE v2 lossless fabrics… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/autonomous-cloud-gpu-slurm-serving-suite.yi-ko-DPO-noprompt-datasetgpu-forecasters-rl-training-poolCompanion artifact for GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization. Code: codezakh/gpu-surrogates.
Training data used to fine-tune the three RL surrogate variants in the paper (correctness, correctness + Brier, correctness + CRPS rewards). Each row is one (anchor, candidate) kernel pair with the candidate's measured speedup relative to the anchor.
No rows for the gdn_chunk_fwd_o pack.
Loading
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/codezakh/gpu-forecasters-rl-training-pool.orca-alpaca-gpt4-math
Dataset Card for "orca-alpaca-gpt4-math"
More Information needed
pallasbench-robust-gpu-a100
PallasBench: Robust Pallas GPU Kernel Benchmark (A100)
39/45 kernels passing on NVIDIA A100 80GB -- the first GPU-focused evaluation of JAX Pallas kernels.
What is this?
PallasBench is a suite of 45 JAX Pallas kernels across 3 difficulty levels. The original kernels were designed for TPU and failed on GPU because Pallas compiles to Triton on NVIDIA hardware, which has strict block size limits that TPU's Mosaic compiler does not.
We fixed all 45 kernels for GPU… See the full description on the dataset page: https://huggingface.co/datasets/EvanOLeary/pallasbench-robust-gpu-a100.gpu-forecasters-discovery-pairsCompanion artifact for GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization. Code: codezakh/gpu-surrogates.
Used to evaluate whether surrogates can identify discovery moments: parent-to-child mutations where the child kernel is much faster than its parent. Each row is one parent-child kernel pair.
Loading
from datasets import load_dataset
# all pairs
ds = load_dataset("codezakh/gpu-forecasters-discovery-pairs", name="combined", split="pairs")… See the full description on the dataset page: https://huggingface.co/datasets/codezakh/gpu-forecasters-discovery-pairs.autonomous-gpu-kernel-triton-cuda-suite-2026
⚡ Autonomous GPU Kernel, Triton & CUDA Architecture Suite (2026)
A Production-Grade, Verifiable Synthetic Corpus for Training Frontier Coding Models (Qwen 3.8, DeepSeek-V3, Llama 3.3)
⚡ Overview & Industry Problem
Modern deep learning accelerators, custom ASICs, and high-performance computing clusters demand specialized, autonomous GPU kernel infrastructure: OpenAI Triton fused kernels, FlashAttention-3 forward/backward online softmax… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/autonomous-gpu-kernel-triton-cuda-suite-2026.Yi-Ko-DPO-Orca-DPO-Pairsgpumode-py2triton-reasoning-v2pdf-BZU-catalog-for-GPU-projectGPU-math-group-qwen3-235b-a22b-2507orca-alpaca-gpt4
Dataset Card for "orca-alpaca-gpt4"
More Information needed
dataset_amlq_v2_gpuYi-Ko-DPO-Orca-DPO-Pairs-loraYi-Ko-DPO-datasetptxas