Yes. io.net supports GPU-accelerated databases like RAPIDS cuDF (GPU DataFrames), BlazingSQL, Kinetica, and OmniSci for analytics workloads that benefit from parallel processing. GPU databases deliver 10-100x faster query performance on large datasets compared to CPU-based databases.

Supported GPU Databases

RAPIDS cuDF (Recommended):

import cudf

# GPU-accelerated DataFrame operations
df = cudf.read_csv("large_dataset.csv")  # 10-50x faster than pandas
result = df.groupby("category").mean()  # GPU-parallelized

BlazingSQL:
- SQL queries on GPU
- 10-100x faster than traditional databases
- Compatible with Apache Arrow

Kinetica:
- Real-time analytics database
- GPU-accelerated aggregations
- Enterprise support available

Use Cases

• Real-time analytics on large datasets
• Time-series data processing
• Log aggregation and analysis
• IoT data streams
• Financial data analysis

Performance

Query Performance (1B row dataset):
- CPU (PostgreSQL): 45 seconds
- GPU (RAPIDS): 2 seconds (22x faster)

Deployment

# Deploy RAPIDS environment
io deploy --image rapidsai/rapidsai:latest \
  --gpu A100 \
  --memory 80GB \
  --name gpu-database

GPU-accelerated databases on io.net — 10-100x faster analytics with RAPIDS.