We are a dedicated group of independent developers and researchers focusing on deep learning framework optimization, large language model inference acceleration, and next-generation cloud infrastructure deployment.
Optimizing PyTorch and TensorFlow execution graphs. We focus on reducing latency and maximizing throughput for complex neural network architectures through kernel fusion and memory management.
Bridging the gap between model size and deployment speed. Specializing in vLLM, TensorRT-LLM, and quantization techniques (INT8/FP4) to run massive models on consumer hardware.
Building scalable AI clusters. We design Kubernetes-based orchestration systems for GPU sharing, serverless inference endpoints, and distributed training pipelines.