datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
quantum-hardware-device-physics
Neura Parse — Quantum Hardware Device Physics: Qubit Design, Coherence, Control & Scaling
A physics- and engineering-deep vertical on how qubits are built, controlled, and scaled across superconducting, trapped-ion, neutral-atom, and spin modalities (plus emerging erasure/biased-noise qubits). Device-physics derivations, coherence-limit analyses, control-stack engineering, and 2025-2026 scaling/interconnect work, with QuTiP/scqubits simulation context — expanding the general… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-hardware-device-physics.modelfit-hardware-dataset
ModelFit: Local LLM Hardware Compatibility Dataset
An open dataset of which local AI models (Ollama) fit which hardware, by
parameter size, quantization, minimum RAM, and estimated memory load, across
Apple Silicon Macs, iPhones, and NVIDIA GPUs.
Maintained by ModelFit. Browse it as an interactive
table at modelfit.io/data; the canonical
machine-readable source is
modelfit.io/api/dataset.
141 models across 24 families (106 with a registry-verified local build, 35 cloud-only… See the full description on the dataset page: https://huggingface.co/datasets/modelfit/modelfit-hardware-dataset.r1-h4-trigger-hardware-15fpsibm-150q-ising-hardware-results
IBM 150-Qubit Ising Hardware Results
Auditable results from a hardware-native, 150-active-qubit Ising/QAOA study on IBM ibm_fez, accompanied by an exact classical baseline and explicit scientific claim boundaries.
Headline evidence
Item
Verified value
Active physical qubits
150
Processor qubits
156
Successful hardware jobs
15
QPU usage
599 seconds
PUBs
547
Shots
2,087,936
QAOA depths
p=1 through p=8
Exact classical optimum
865 / 873, MIP… See the full description on the dataset page: https://huggingface.co/datasets/sankalpsthakur/ibm-150q-ising-hardware-results.ai-inference-hardware-economics-2026
🚀 2026 AI Inference & Hardware Economics Telemetry Index
This repository hosts the official open-access empirical telemetry dataset for 2026 AI Inference, Silicon Architecture, and Hardware Economics, curated by EyesTech Systems & FinOps Intelligence.
Original Research Investigation:For the complete whitepaper, interactive latency calculators, and per-token TCO models, see the flagship publication at:👉… See the full description on the dataset page: https://huggingface.co/datasets/devidasmishra/ai-inference-hardware-economics-2026.africa-synth-telecom-hardware-sensor-data-nigeria
Africa Synth Telecom Hardware Sensor Data Nigeria | Africa (Electric Sheep Africa metadata inventory)
Size category: 100K<n<1M - Formats: parquet - Sector: energy - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-telecom-hardware-sensor-data-nigeria.africa-synth-telecom-hardware-sensor-data-nigeria
Africa Synthetic Telecom Hardware Sensor Data Nigeria (TsFile)
This dataset is an Apache TsFile conversion of electricsheepafrica/africa-synth-telecom-hardware-sensor-data-nigeria, a synthetic Nigerian telecom tower hardware sensor dataset with temperature, power, voltage, humidity, vibration, health-status, and alert readings.
Source Dataset
Original dataset: electricsheepafrica/africa-synth-telecom-hardware-sensor-data-nigeria
Source files:… See the full description on the dataset page: https://huggingface.co/datasets/THULab/africa-synth-telecom-hardware-sensor-data-nigeria.Mlops-Hardware-Carbon-Benchmarks
MLOps Hardware Benchmarks & Carbon Emissions
Dataset Description
This dataset contains 3,000 empirical, synthetic profiling records tracking large language model execution runs across diverse modern datacenter and consumer accelerators (including NVIDIA H100, A100, RTX 4090, and A10G). It captures token volumes, execution speeds, physical power utilization metrics, and overall computed carbon footprint weights.
Purpose and Impact
As deep learning… See the full description on the dataset page: https://huggingface.co/datasets/sohaibdevv/Mlops-Hardware-Carbon-Benchmarks.piper_scoop_v1_20260721_154509This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"joint_1.pos",
"joint_2.pos",
"joint_3.pos",
"joint_4.pos",
"joint_5.pos",
"joint_6.pos",
"gripper.pos"
],
"shape": [… See the full description on the dataset page: https://huggingface.co/datasets/hardware-pathon-ai/piper_scoop_v1_20260721_154509.openarm_dataset_test_20260920_214839This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 15,
"features": {
"action": {
"dtype": "float32",
"names": [
"joint_1.pos",
"joint_2.pos",
"joint_3.pos",
"joint_4.pos",
"joint_5.pos",
"joint_6.pos",
"joint_7.pos",
"gripper.pos"
]… See the full description on the dataset page: https://huggingface.co/datasets/hardware-pathon-ai/openarm_dataset_test_20260920_214839.box_closing_v2.5_both_new_hardwareThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"right_joint_1.pos",
"right_joint_2.pos",
"right_joint_3.pos",
"right_joint_4.pos",
"right_joint_5.pos",
"right_joint_6.pos",
"right_joint_7.pos"… See the full description on the dataset page: https://huggingface.co/datasets/Anteid11/box_closing_v2.5_both_new_hardware.box_closing_v2.5_both_new_hardware_recomputed_statsThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"right_joint_1.pos",
"right_joint_2.pos",
"right_joint_3.pos",
"right_joint_4.pos",
"right_joint_5.pos",
"right_joint_6.pos",
"right_joint_7.pos"… See the full description on the dataset page: https://huggingface.co/datasets/Anteid11/box_closing_v2.5_both_new_hardware_recomputed_stats.hardware-profiles
Hardware Profiles
Phone hardware profiles for estimating mobile LLM inference speed.
Snapdragon 865 (Samsung S20 FE) is the verified baseline.
🚀 dispatchAI
hardware_pricesneuromorphic_hardware_power_benchmarks