
sirius
GPU-native composable analytics engine
The Lens
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
Updated Oct 2026
Sirius makes DuckDB run your SQL on an NVIDIA GPU. Load it as an extension and your existing queries get intercepted and executed on the graphics card with no rewrites, and anything it can't handle falls back to the CPU. It's Apache 2.0, built mostly by NVIDIA engineers with UW-Madison, and nobody sells a paid version.
The hardware gate is real: Linux, an NVIDIA Turing-or-newer GPU, recent CUDA drivers, and a kernel and filesystem that allow io_uring and direct I/O. No Macs, no AMD, no Windows. There are no packaged releases either. You build from source with Pixi, and your DuckDB version has to match the one Sirius was built against exactly.
This is for data teams already living in DuckDB who own or rent NVIDIA GPUs and want big analytical queries to finish faster. Everyone else should stay on plain DuckDB (duckdb/duckdb). For GPU dataframes instead of SQL, look at cuDF. DuckDB is the only database it supports today; StarRocks and Doris are roadmap items.
The catch: the headline ClickBench records came from the old engine, which has since been rewritten. And the CPU fallback is silent, so a query with window functions or nested types quietly runs on the CPU and you won't know you lost the speedup.
Free vs Self-Hosted vs Paid
fully freeFree: Everything. Apache-2.0, no company, no cloud, no enterprise tier.
Self-hosted: The real cost is hardware. You need Linux, an NVIDIA GPU with compute capability 7.5 or newer, CUDA 12 or 13 drivers, io_uring allowed (many locked-down container platforms block it) and direct I/O on the storage holding your Parquet files. Build from source with Pixi; there are no versioned releases, only rolling latest and stable builds.
Paid: None. Your bill is GPU servers or cloud GPU instances.
Free under Apache 2.0. The cost is NVIDIA GPU hardware or cloud GPU time, plus the effort of building it from source.
What to do by team size
- Solo
- Skip unless you already have an NVIDIA box on Linux; plain DuckDB is enough
- Small team
- free; try it only if your DuckDB queries are slow and you have GPU access
- Medium team
- free; worth a benchmark on rented GPUs before committing
- Large team
- free; promising for GPU analytics, but wait for packaged releases before production
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License: Apache License 2.0
Use freely. Patent grant included.
Commercial use: ✓ Yes
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