AI Training Frameworks
PyTorch and TensorFlow, tuned for the interconnect
- PyTorch
- TensorFlow
- NCCL
- RCCL
- ZeRO-3
- FP8
- BF16
Industry
AI Research Labs & Model Builders
Training frameworks and inference engines tuned to the silicon underneath them.
A training run is a distributed systems problem before it is a machine learning problem. We build the communicators, partitioning, mixed-precision trainers, and elastic scheduling that keep thousands of accelerators busy, and the serving engines that return the model’s answer in the fewest possible microseconds.
What we see
- All-reduce bandwidth that caps how large a model can be trained
- Checkpointing and node failures that stall multi-week runs
- Serving costs that scale linearly with traffic
What we bring
AI Inference Optimization Engines
FP16 to INT4 with under 1% accuracy loss
- TensorRT
- ONNX Runtime
- PTQ/QAT
- INT4
- Knative
Hyperscale Data Center Operating Systems
A kernel written for the silicon it runs on
- Linux
- NUMA
- CXL memory
- PMEM
- SPDK
Cloud Infrastructure Orchestration
Every cluster on every cloud, managed as one fleet
- Kubernetes
- Karpenter
- ArgoCD
- Flux
- Multi-cloud
How we help
What we bring to ai labs.
The systems this industry draws on, layer by layer.
02
AI Inference Optimization
A serving engine built on TensorRT and ONNX Runtime with custom optimization passes.
What we build03
Hyperscale Operating Systems
A custom Linux kernel distribution and user-space runtime.
What we build04
Cloud Orchestration
A Kubernetes-based control plane extended with multi-cluster federation (managing K8s across AWS/Azure/GCP/on-prem), auto-scaling agents (Karpenter), FinOps cost-monitoring dashboards, disaster recovery controllers (automated failover with RPO <15min), and GitOps agents (ArgoCD/Flux) for declarative state management.
What we buildBring 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.