C.2 · Artificial Intelligence & Machine Learning
AI Inference Optimization Engines
FP16 to INT4 with under 1% accuracy loss
What we build
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).
Capabilities
- Serving engine built on TensorRT and ONNX Runtime with custom optimization passes
- Post-training and quantization-aware training (PTQ/QAT) from FP16 to INT4 with under 1% accuracy loss
- Graph optimization through operator fusion and kernel tiling
- Dynamic batching schedulers and speculative decoding routers
- Serverless scaling adapters with Knative integration
Related services
How it connects
Where it sits in the stack.
This system, and the two it hands off to. None of them can be optimized alone.
AI Inference Optimization
A serving engine built on TensorRT and ONNX Runtime with custom optimization passes.
AI Training Frameworks
Extensions and optimizations for PyTorch and TensorFlow.
AI & Machine Learning · see serviceSerDes & Chip IP
Mixed-signal PHY IP blocks including PAM4/NRZ transceivers up to 224Gbps per lane, adaptive equalizers (FFE/DFE) for >40dB channel loss, clock-data recovery (CDR) circuits, and built-in self-test (BIST) for link health monitoring.
Semiconductor & Silicon · see serviceBring us the whole stack.
Tell 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.