CORE TECH RESEARCH

Core Technology

Pushing the boundaries of inference performance through kernel-level engineering and systems optimization.

Kernel Engineering

We specialize in low-level kernel development for modern GPUs and SoCs, focusing on maximum hardware utilization.

CUTLASS-based Tensor Core programming

GEMM and Attention kernel optimization

Custom memory hierarchy management

Profiling-driven bottleneck elimination

Systems Optimization

Beyond kernels, we optimize the entire inference stack for production reliability and throughput.

Dynamic batching & scheduling

Memory-efficient KV cache management

Distributed inference orchestration

Quantization-aware performance tuning

MEASURABLE ALPHA

We eliminate the 'Software Tax' imposed by high-level Python framework abstractions. By controlling Tensor Cores directly via C++ and CUTLASS, we strive for benchmarks that push hardware utilization near its 100% theoretical limit.

THROUGHPUT

tokens/sec, batch, decode

LATENCY

p50/p95/p99

UTILIZATION

tensor core, memory, sm

* Performance varies by workload, hardware, and baselines. We publish reproducibility conditions and metrics (e.g., p50/p99) alongside results.

* Detailed benchmark environments (infrastructure/hardware configs) and results can be shared under NDA with partners.