GPU Computing
Accelerator performance from kernel to cluster.
GPU Computing covers the low-level performance work that makes training and inference economical: kernel fusion, communication collectives, memory planning, and multi-accelerator scheduling. It is the silicon-aware foundation of Distributed Cloud and Inference Infrastructure.
Kernels
Custom ops for hot paths in serving and training.
Collectives
Efficient multi-GPU and multi-node communication.
Memory
Paging, recomputation, and fragmentation control.
Benching
Continuous performance regression suites.
Performance is a research problem
We treat FLOPs utilization and communication overlap as measurable science. Wins at the kernel layer multiply across every customer workload on the fabric.
Heterogeneous by design
Real fleets mix GPU generations and emerging NPUs. Our abstractions expose capabilities without forcing every model author to rewrite for each SKU.
Tied to Distributed Computing Lab
Lab researchers and platform engineers share the same benchmark farms so academic ideas become production kernels without a multi-year translation tax.