About Slento Systems
Making enterprise hardware optimization accessible to every team that runs heterogeneous compute.
Our Mission
Modern compute infrastructure is heterogeneous. Organizations run AMD GPUs alongside NVIDIA accelerators, distribute workloads across CPU clusters and FPGAs, and manage fleets that span data centers and cloud regions.
Most optimization tools assume a uniform environment. They tune for one vendor, one architecture, one workload shape. That leaves significant performance — and budget — on the table.
Slento Systems builds software that understands your entire fleet. Our systems profile hardware behavior, learn workload patterns, and deliver optimized configurations that work across vendors and architectures. No vendor lock-in. No black boxes.
What drives us
- Hardware optimization should not require a PhD
- Performance data belongs to the teams that generate it
- AI models should explain their recommendations
- Open research makes the whole ecosystem better
Our Technology
Three interconnected systems that turn raw hardware telemetry into actionable optimization.
Behavior Atlas
A structured map of how each device performs across parameter spaces. The atlas captures invariants, performance cliffs, and optimal operating regions through systematic profiling.
Backed by persistent storage. Survives reboots, hardware swaps, and firmware updates.
JEPA Neural Architecture
Joint Embedding Predictive Architecture models learn compressed representations of hardware behavior. Given a workload description and hardware fingerprint, JEPA predicts performance without running benchmarks.
Compact models (under 250K parameters) that run on any device in the mesh.
Distributed Mesh
Nodes form a self-organizing mesh that distributes profiling, learning, and optimization across the fleet. No central server required. Each node contributes to and benefits from the collective intelligence.
Peer-to-peer architecture with no single point of failure.
Open Research
We believe open research accelerates the entire hardware optimization ecosystem. Our findings on AMD GPU behavior are published for the community.
Our RDNA3 behavior atlas — covering 81,000+ data points across 11 kernel types — is the most comprehensive public characterization of the AMD Radeon RX 7900 XTX architecture available. These findings inform our commercial products and are freely available to researchers and engineers.
This is not altruism as marketing. Open benchmarks hold us accountable. When we claim a 3x median speedup, anyone can verify the methodology and reproduce the results on their own hardware.
Want to learn more?
We are happy to discuss how Mesh Optimizer can fit your infrastructure.
Get in Touch