Category C · 2 services
Training that scales across thousands of accelerators and inference that serves in microseconds.
Model quality is bounded by how fast gradients move between GPUs and how many tokens a serving engine can return per watt. We build the communicators, partitioning schemes, quantizers, and schedulers that set those limits.
PyTorch and TensorFlow, tuned for the interconnect
Extensions and optimizations for PyTorch and TensorFlow. Includes custom NCCL/RCCL communicators for multi-GPU all-reduce, ZeRO-3 partitioning (model states, gradients, and optimizers), mixed-precision trainers (FP8/BF16), asynchronous checkpointing, and elastic training that dynamically adds/removes nodes mid-run.
FP16 to INT4 with under 1% accuracy loss
A serving engine built on TensorRT and ONNX Runtime with custom optimization passes. Includes quantization algorithms (PTQ/QAT to compress FP16→INT4 with <1% accuracy loss), graph-optimization passes (operator fusion, kernel tiling), dynamic batching schedulers, speculative decoding routers, and serverless scaling adapters (Knative integration).
How it connects
The systems in this area, and what each one hands to the next.
A serving engine built on TensorRT and ONNX Runtime with custom optimization passes.
What we buildTell us where latency is costing you, from the die to the data center to the control room. An architect replies with a first read of the problem, not a sales deck.
