FRAMEWORKS
PyTorch / TF / JAX
PRECISION
FP16 / BF16 / FP32
MAX GPUs
64
STORAGE
NVMe Scratch
-- CAPABILITIES --------
DISTRIBUTED TRAINING
Scale training across up to 64 GPUs with automatic sharding. Data parallel, model parallel, and pipeline parallel strategies supported natively.
MIXED PRECISION
Train in FP16, BF16, or FP32 with automatic loss scaling. Reduce memory usage by 50% and accelerate training by 2-3x with minimal accuracy impact.
CHECKPOINT MANAGEMENT
Automatic periodic checkpointing to persistent storage. Resume from any checkpoint after preemption or failure. Distributed checkpoint support.
NVMe SCRATCH
High-speed local NVMe storage for training data staging. Eliminate I/O bottlenecks with TB-scale scratch space per training pod.
-- USE CASES --------
▸Foundation model pre-training and fine-tuning
▸Reinforcement learning at scale
▸GAN training for image and video generation
▸Neural architecture search and AutoML