Large-Scale Model Training
End-to-end infrastructure for frontier-model training runs.
Training foundation models at scale exposes bottlenecks in interconnect, utilization, resource allocation, and observability. ApeTops delivers a multi-stage solution that compresses time-to-train and maximizes cluster efficiency.
Background & challenges
Architecture components
High-density compute clusters
Multi-GPU NVIDIA H200 / H100 / B200 nodes with NVLink and NVSwitch.
Distributed training stack
PyTorch FSDP, DeepSpeed ZeRO, Megatron-LM, and OneFlow — pre-validated on the fabric.
Elastic scheduler
Volcano + Kueue for gang scheduling; checkpoint-restore across capacity tiers.
GPU resource pooling
MIG slicing, fractional scheduling, and cross-user priority queues.
Topology-aware placement
Locality-aware placement minimizes cross-rack traffic and stragglers.
Heterogeneous fusion
Blend Hopper, Ampere, and Blackwell in one logical cluster for elastic capacity.
Implementation steps
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1
Workload profiling
Instrument the run to identify the bottleneck (compute, memory, fabric, or I/O).
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2
Architecture design
Fabric sizing, storage tiering, and redundancy plan tied to your time-to-train target.
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3
Cluster build-out
Stand up racks, cabling, fabric commissioning, and burn-in.
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4
Pipeline deployment
Port your training code; validate checkpointing and resume semantics.
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5
Monitoring & SRE
24/7 NOC with auto-remediation and weekly performance reports.
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6
Continuous tuning
Quarterly architecture reviews to keep utilization north of 70%.
Advantages & value
- Compress time-to-train by 30–50% vs. commodity hybrid-cloud setups.
- Cluster utilization stays above 70% on average.
- Flexible capacity — scale from 8 GPUs to >1K seamlessly.
- No vendor lock-in: open formats, open schedulers, portable checkpoints.
- Sustained 99.95% fabric availability on our managed footprint.
Let's architect your deployment
Our solutions team will scope, price, and stand up the infrastructure for you.
Talk to a solutions architect