Enterprises processing hundreds of gigabytes of clickstream data or building retrieval-augmented generation (RAG) vector stores have traditionally relied on sprawling 64-node CPU clusters running Apache Spark.
However, modern GPU computing frameworks like NVIDIA RAPIDS cuDF and Polars GPU can perform the exact same joins, aggregations, and string transformations on a single 8-GPU node at a fraction of the cost.
On NovaPod, data engineering teams can launch pre-tuned RAPIDS notebooks with GPU-accelerated Arrow memory buffers, eliminating serialization bottlenecks between storage and compute.
Generating 768-dimensional embeddings for 10 million documents drops from a multi-day CPU batch job to a 25-minute execution run on an 8x H100 cluster.
Início rápido pela CLI da NovaPod
import cudf, polars as pl
import cupy as cp
# Zero-copy GPU columnar ingestion on NovaPod
df = cudf.read_parquet("/workspace/telemetry_100M.parquet")
grouped = df.groupby(["device_id", "region"]).agg({"latency": "mean", "errors": "sum"})
print("Processed 100M rows in 1.2 seconds on H100")O que a arquitetura mudaTabular data ingestion and vector indexing on high-VRAM GPU nodes drastically outperform bulky CPU clusters. NovaPod's pre-configured RAPIDS environments deliver instant access to cuDF, cuML, and GPU-native FAISS indexing.