GPU-Native from Day One

Why SQream Has Been Ready for This Accelerated Moment

When Jensen Huang, CEO of NVIDIA, stepped on stage at GTC DC, he delivered a powerful validation of a transformation that is reshaping the data world:

“Accelerated computing – its moment has now arrived. However, accelerated computing is a fundamentally different programming model. You can’t just take CPU software written by hand and execute it sequentially, and put it onto GPU and have it run properly. In fact, if you did that, it actually runs slower. You have to reinvent new algorithms. You have to create new libraries. You have to rewrite the application…”

He wasn’t talking theory. He was outlining the technical imperative facing modern compute infrastructure: True acceleration means building it differently, not just trying to make CPU software run on GPUs. The revolution isn’t in GPUs alone – it’s in how they’re used, programmed, and optimized.

At SQream, that statement resonates deeply. Why? Because we didn’t retrofit our database technology to exploit GPU horsepower. We built it natively for it – aligning from the start with the exact programming model, architectural thinking, and ecosystem Jensen described.

The Technical Core: SQream’s Native GPU Foundation

From its foundation, SQream was engineered to harness GPU parallelism as a first-class architecture – not as a bolt-on. That means:

• Massive parallelization of queries across thousands of GPU threads, allowing for linear scale on high-volume data workloads.
• SQream uses a GPU-native execution model that parallelizes operations across thousands of GPU and CPU threads, efficiently handling large-scale queries with high concurrency and minimal resource contention.
• Columnar storage and adaptive compression for optimal scan performance – without sacrificing precision.
• Seamless compatibility with NVIDIA CUDA and RAPIDS libraries, allowing data engineers to use familiar toolchains (like cuDF, cuML, and XGBoost) for ML training inside the database, without data movement.

These design decisions aren’t just technical niceties – they’re what enable SQream to outperform CPU-bound systems by orders of magnitude.

Real-World Impact: When Data Size Isn’t a Limitation

SQream’s native GPU architecture delivers a unique advantage in industries drowning in data:

• Semiconductor manufacturers are utilizing SQream to analyze sensor and production line data from wafer fabrication processes, often generating hundreds of terabytes of data per day. By running complex yield analytics and anomaly detection at scale, they can uncover defect patterns in near-real time – insights that traditional CPU-based systems would take hours or even days to deliver.
• Telecoms are accelerating CDR analysis from hours to minutes, enabling near real-time subscriber insights across petabyte-scale datasets.
• Financial institutions are slashing time-to-insight on high-frequency trade data, helping them stay ahead of risk in volatile markets.

What’s key here is batch-scale ML and SQL / Python analytics on the same infrastructure. With SQream, you don’t need to sample down your data. You train and infer on full production volumes – directly in-database, with familiar SQL and Python interfaces.

Accelerated Computing Isn’t a Buzzword. It’s a Build Philosophy.

Jensen Huang made it clear: “The GPU is important, but without a programming model that sits on top of it, and without dedication to that model… developers wouldn’t target this computing platform.”

This is exactly what SQream has done – dedicating over a decade to building a platform. that doesn’t just run on GPUs but thrives on them. Our team built a new engine for the accelerated era, and that shows in performance benchmarks, TCO reductions, and operational simplicity.

• 100+ TB datasets? Not a problem – no need for data sharding or duplication.
• High concurrency? Yes – SQream supports multiple users and large analytical queries without resource contention.
• Complex queries with joins, aggregations, window functions? Executed seamlessly in parallel on the GPU.

And it’s not just faster – it’s cheaper. With less hardware, less energy, and faster results, our customers routinely report 1/10 the cost and 5x the speed compared to legacy systems.

Final Thought

When Jensen Huang says, “Accelerated computing’s moment has now arrived,” we couldn’t agree more. We’ve been living in that moment for years.

We anticipated the shift, and built for it – not around it. SQream is aligned perfectly with the accelerated computing model, CUDA libraries, and programming future that NVIDIA is now heralding.

If you’re working with large-scale data and feeling the limits of your CPU-based infrastructure, SQream is ready – now – to help you cross the performance chasm. Let us show you what acceleration really means.