Every layer of our stack is designed under production constraints. We don't abstract away complexity — we engineer through it.
Three core components that power every Threnlabs product.
Custom CUDA kernels and memory management optimized for batch inference at scale. Our runtime achieves superior throughput on standard vision and language workloads by implementing direct cuDNN primitives with fused kernel execution and zero-copy tensor passing between pipeline stages.
The runtime exposes a simple engine API while abstracting stream-level parallelism, kernel fusion, and async memory management. You write model inference code. We handle everything underneath.
Priority-aware job scheduling with GPU memory defragmentation and preemptive context switching. Cosmos Scheduler manages the full lifecycle of inference jobs across a cluster — admission control, priority queuing, SLA-aware preemption, and hardware-aware placement.
Jobs are represented as DAGs with per-node SLA constraints. The scheduler solves bin-packing under memory and latency constraints in real time, rebalancing as workloads shift without service interruption.
High-throughput data ingestion with format-agnostic preprocessing. Handles extreme peak throughput with automatic backpressure management, schema inference, and zero-copy reads from object storage, message queues, and streaming sources.
The pipeline is stateless by design — preprocessing logic is expressed as composable transforms, making it trivial to add new data sources or preprocessing steps without affecting downstream inference.
Cosmos adapts to what you already use — no migration required.