Precision Distributed
Training Topology
Eliminate fragmented network hops and compute latency bottlenecks. Tensor Axiom maps physical NVLink, AMD Infinity Fabric, RDMA, and NUMA topologies into hardware topology graphs to maximize linear scaling and lift sustained GPU utilization across heterogeneous clusters.
Contiguous Fabric Mapping & Elastic Preemption
Indexed Topology Graphs & Interconnect Mapping
Maps multi-dimensional cluster fabrics, including PCIe switches, InfiniBand rails, NVLink domains, and AMD Infinity Fabrics, into indexed hardware topology graphs. The scheduler identifies contiguous allocations in under one millisecond across 10,000+ accelerators, aligning Data, Tensor, and Pipeline parallel rings directly to physical fabric boundaries.
High-Bandwidth Fabric Preservation
Unifies NVIDIA and AMD ecosystems under a single hardware-agnostic overlay while actively guarding high-bandwidth islands. The control plane automatically steers single-accelerator and small-scale jobs onto standard PCIe nodes, preserving contiguous NVLink and AMD Infinity Fabric (xGMI) blocks unfragmented for massive foundation model runs.
Synchronized Scheduling & Deadlock Prevention
Eliminates partial resource allocations and cluster-wide deadlocks. Distributed training collectives are scheduled atomically across tightly coupled fabric boundaries or held in lock-free readiness queues, guaranteeing that thousands of GPUs never sit idle waiting for delayed resource provisioning.
Dominant Resource Fairness (DRF) Quotas
Enforces hierarchical multi-tenant quotas using Dominant Resource Fairness across compute cores, HBM capacity, and RDMA interconnect bandwidth. Organizations receive guaranteed baseline floors alongside elastic burst ceilings that automatically harvest idle cluster capacity.
Graceful Preemption & Capacity Reclamation
Replaces static cluster partitioning with a Reserved Baseline Floor and an Elastic Shared Pool. When daytime real-time inference surges or critical priority jobs arrive, the scheduler triggers asynchronous model checkpointing on lower-priority batch runs and gracefully reclaims GPUs in seconds without lost training progress.
Direct RDMA/NUMA Pinning & Thermal Steering
Maps CPU socket and PCIe root-complex topologies to pair accelerators with direct NIC-to-GPU RDMA pathways, eliminating cross-socket CPU traversal bottlenecks. Simultaneously ingests real-time silicon health metrics to route long-running checkpoints around thermally throttled or ECC-faulting nodes.
Cross-Cluster Spanning Placements & Regional Gateway Federation
Transparently spans mega-scale training runs across multiple isolated Kubernetes clusters managed by a unified regional control gateway. By abstracting single-cluster control-plane limits while maintaining strict awareness of inter-cluster optical spine bandwidth, Tensor Axiom enables massive synchronized training collectives across disaggregated datacenter halls.