Training Clusters
Multi-GPU compute clusters wired with InfiniBand or RoCE for distributed deep-learning training where the bottleneck is your research, not the fabric. GPUDirect RDMA, job scheduling, and automated checkpointing mean a node failure costs an epoch, not the entire run.
Overview
One GPU trains slowly; many GPUs train even slower unless they can talk to each other efficiently. Training Clusters are purpose-built multi-GPU nodes with NVIDIA NVLink and NVSwitch interconnects for intra-node communication at 900 GB/s and InfiniBand HDR (200 Gbps) or RoCE v2 (100 Gbps) fabric for inter-node gradient synchronisation via GPUDirect RDMA. Designed for distributed deep-learning workloads on NVIDIA A100, H100, H200, and B200 GPUs, these clusters ensure that accelerator utilisation stays high and the bottleneck is the research, not the network fabric. Every cluster is deployed at rack scale with high-density cooling for sustained TDP workloads, tiered checkpoint storage (NVMe burst tier with automated offload to object store), and Slurm or Kubernetes job scheduling with GPU partitioning and fair-share queueing across teams.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We design, deploy, and operate multi-GPU training clusters optimised for distributed deep learning at scale. Every cluster comprises 4-GPU or 8-GPU nodes interconnected via NVLink 4.0/NVSwitch within each node and InfiniBand HDR (200 Gbps) or RoCE v2 (100 Gbps) between nodes, with GPUDirect RDMA for direct GPU-to-GPU data transfer without CPU involvement. Clusters are deployed at rack scale with liquid or high-velocity air cooling, redundant power and fabric, and tiered storage (NVMe burst layer for checkpoint I/O, NAS or parallel file system for dataset access, and object store for long-term model and data retention). Job scheduling runs on Slurm or Kubernetes with GPU partitioning, pre-emptible job queues, and fair-share allocation across teams. Checkpointing saves model state at configurable intervals to tiered storage, so a node failure costs epochs, not the entire training run. We manage the full infrastructure — fabric tuning, NCCL configuration, storage provisioning, and cluster monitoring — so your ML engineers launch distributed training jobs without becoming cluster administrators.
Why work with us
NVLink and InfiniBand fabric
NVLink 4.0 at 900 GB/s intra-node and InfiniBand HDR at 200 Gbps inter-node with GPUDirect RDMA. Gradient synchronisation completes in milliseconds, not seconds — keeping GPU utilisation above 90% on large-scale distributed training.
Latest NVIDIA GPU generations
NVIDIA A100 80 GB, H100 80 GB SXM, H200 141 GB, and B200 Blackwell GPUs with NVLink and NVSwitch. Each node delivers the full GPU memory bandwidth and interconnect for large-model training.
Resilient checkpointing at scale
Model state checkpointed at configurable frequency to tiered NVMe-to-object-store path. A node or fabric failure recovers from the last checkpoint, costing minutes of re-computation, not days.
Scheduled and fair-share across teams
Slurm or Kubernetes GPU scheduling with fair-share queues, priority pre-emption, and dedicated node pools for critical training runs. Teams share the cluster without fighting for accelerator time.
Key benefits
What this solution delivers for your business.
Dramatically faster training at scale
NVLink fabric at 900 GB/s and InfiniBand at 200 Gbps mean gradient synchronisation is not the bottleneck. A 70B-parameter model distributed across 64 GPUs trains in days on H100 clusters — versus weeks on loosely coupled GPU nodes.
High GPU utilisation, lower cost per epoch
GPUDirect RDMA and NCCL-optimised topology keep accelerators busy computing instead of idle waiting for network transfers. Higher utilisation means more epochs per GPU-hour and lower effective cost per training run.
Resilience against multi-hour job loss
A node failure on a 72-hour training run without checkpointing means starting over. Tiered checkpointing at configurable intervals means recovery from the last checkpoint — typically minutes of lost progress, not days.
Cluster sharing without team conflict
Fair-share scheduling with priority queues means each team gets guaranteed GPU time for critical runs, with burst capacity from unused allocation. No more informal arrangements or manual accelerator assignment.
What's included
Part of this managed service.
Multi-GPU compute nodes
4-GPU and 8-GPU nodes with NVIDIA NVLink 4.0 and NVSwitch for intra-node GPU communication at 900 GB/s. Deployed at rack scale with liquid or high-velocity air cooling for sustained 700W+ TDP per GPU.
- NVIDIA H100 80 GB SXM with NVLink 4.0
- NVIDIA H200 141 GB for large-model training
- NVIDIA B200 Blackwell for next-generation workloads
- Rack-scale density with high-performance cooling
High-speed cluster fabric
InfiniBand HDR (200 Gbps) or RoCE v2 (100 Gbps) inter-node fabric with GPUDirect RDMA for zero-copy GPU-to-GPU data transfer. NCCL-optimised topology with SHARP in-network reduction for gradient aggregation.
- InfiniBand HDR 200 Gbps fabric
- RoCE v2 at 100 Gbps alternative
- GPUDirect RDMA peer-to-peer transfers
- NCCL SHARP in-network reduction
Job scheduling and GPU partitioning
Slurm or Kubernetes job scheduler with GPU partitioning, fair-share queues, pre-emptible job tiers, and dedicated node pools. Integration with cluster autoscaler for elastic capacity addition.
- Slurm workload manager
- Kubernetes with GPU operator
- Fair-share queue with priority pre-emption
- Dedicated and burst node pools
Tiered checkpoint storage
Model state checkpointed to local NVMe burst tier (microsecond latency), automatically offloaded to parallel filesystem or object store. Configurable checkpoint frequency with asynchronous I/O to minimise training stalls.
- NVMe burst tier for checkpoint writes
- Automated offload to object store
- Configurable checkpoint interval
- Asynchronous checkpoint I/O path
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
Large language model pre-training
Pre-train a 70B-parameter LLM on 1 trillion tokens using 64x H100 GPUs across 8 nodes with NVLink and InfiniBand fabric. Training completes in under 14 days with checkpointing every 1000 steps for resilience against node failures.
Multi-modal model fine-tuning
Fine-tune a vision-language model on a multi-modal dataset using 16x A100 GPUs across 2 nodes. The cluster provisions for the fine-tuning job via Slurm scheduling and releases the capacity back to the shared pool on completion.
Questions buyers actually ask
How is this different from GPU as a Service?
GPUaaS provisions single or paired GPUs on demand for individual jobs. Training Clusters are many GPUs wired together with high-speed fabric for distributed training of a single large model. Use GPUaaS for experimentation and inference; use Training Clusters for large-scale pre-training and fine-tuning.
What interconnect speed do I actually need?
For single-node multi-GPU training (4-8 GPUs), NVLink 4.0 at 900 GB/s is sufficient. For multi-node training, InfiniBand HDR at 200 Gbps with GPUDirect RDMA is recommended for models above 7B parameters. RoCE v2 at 100 Gbps works for smaller distributed workloads.
How do you handle node failures during long training runs?
Checkpointing at configurable intervals saves model state, optimizer state, and learning rate schedule to tiered NVMe storage. On node failure, the job restarts from the last checkpoint, recovering minutes of computation instead of losing days. We also maintain spare nodes in the cluster for automatic replacement.
Can multiple teams share the same cluster?
Yes — that is the purpose of the Slurm or Kubernetes scheduler. Teams get fair-share GPU time with priority queues for critical runs. Unused allocation from one team is available as burst capacity for others, keeping the expensive silicon fully utilised.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.