Solutions
·Data Processing
GPU-accelerated
data pipelines.
Process terabytes of training data with RAPIDS cuDF instead of pandas. Run Dask-CUDA for distributed ETL, build feature engineering pipelines, and prepare datasets for model training — all on GPU hardware that turns hours into minutes.
Why Runcrate
Your data pipeline bottleneck is the CPU.
RAPIDS cuDF for ETL
Drop-in pandas replacement that runs on GPU. Read Parquet, CSV, and JSON at GPU speed. Group, join, and aggregate terabyte-scale DataFrames in seconds.
Dask-CUDA distributed processing
Scale beyond a single GPU with Dask-CUDA. Distribute ETL across multiple GPUs on a single node or across your cluster for massive datasets.
Training data preparation
Clean, deduplicate, and tokenize training corpora on GPU. Run MinHash deduplication, quality filtering, and text normalization at scale before training.
GPU data augmentation
Run image transforms, audio preprocessing, and synthetic data generation on GPU. DALI, Albumentations-GPU, and custom CUDA kernels all supported.
Feature engineering at scale
Compute embeddings, TF-IDF, and numerical features with cuML. Build feature stores backed by GPU-accelerated computation for real-time and batch pipelines.
Seamless storage integration
Mount S3, GCS, or attach high-speed NVMe volumes. Stream data in and out without copying entire datasets. Persist intermediate results between pipeline stages.
Hardware
Matched to your
pipeline workload.
High memory bandwidth for data shuffling, large VRAM for in-GPU DataFrames, and fast NVMe for spill-to-disk operations.
H200141 GB HBM3e · 4.8 TB/s bandwidthLargest in-GPU DataFramesH10080 GB HBM3 · 3.35 TB/s bandwidthHigh-throughput ETLA10080 GB HBM2e · 2 TB/s bandwidthCost-effective batch jobsL40S48 GB GDDR6X · 864 GB/s bandwidthLightweight preprocessingHow It Works
From raw data to training-ready.
Connect your data sources
Mount S3 buckets, GCS, or upload directly. Attach NVMe storage for local-speed access. Your data stays where you need it.
Build your GPU pipeline
Use RAPIDS cuDF for transforms, Dask-CUDA for distribution, and cuML for feature engineering. Or bring your own scripts — full root access, any library.
Export clean datasets
Write processed Parquet, tokenized corpora, or feature stores back to cloud storage. Feed directly into your training pipeline on the same cluster.