From single-node fine-tuning on 8x L40S to 128-GPU H100 clusters over NDR InfiniBand — we rack, cable, tune NCCL, and hand over a system that trains at peak from day one.
PyTorch, DeepSpeed/FSDP, Megatron-LM, NVIDIA NeMo, JAX/Flax — pre-installed and version-pinned on request.
NDR 400G with 1 IB per GPU, GPU-Direct RDMA, and pre-tuned NCCL topology files. No fabric debugging on day one.
BeeGFS, Lustre, or Weka tiers for high-throughput dataset reads. Your training loop is never waiting on storage.
Match the GPU to the workload. LoRA/QLoRA fine-tuning on L40S, dense pretraining on H100, long-context and MoE on H200.
Quoted per cluster with InfiniBand fabric design, parallel FS, scheduler, and an SRE engagement to bring it online.
W&B, MLflow, Comet, or Determined — your choice, installed and integrated with your existing accounts.
Designed for 1+ TB checkpoint snapshots without stalling training — local NVMe scratch + parallel FS commit.
Rule of thumb: with ZeRO-3 + activation checkpointing, an 8x H100 (80GB) node trains up to ~30B parameter dense models in BF16 from scratch, or fine-tunes 70B with LoRA/QLoRA. 8x H200 (141GB) roughly doubles that. Anything larger needs multi-node tensor + pipeline parallelism.
Yes if you want it. Our DevOps add-on pre-installs PyTorch + DeepSpeed/FSDP, Megatron-LM, NVIDIA NeMo, NCCL tuning, and your preferred experiment tracker (W&B, MLflow, Comet). Or take a clean bare-metal box and stack it your way.
Yes. We deploy 16, 32, 64, 128-GPU clusters over NDR 400G InfiniBand with rail-optimized topology, parallel filesystem (BeeGFS/Lustre/Weka), and shared scheduler (Slurm or Kubernetes). Quoted per build.
Tell us model size, dataset shape, and timeline — we'll size the right node count and fabric.