I'm building a distributed training cluster for large language models

That’s a big and exciting project. A good way to approach a distributed LLM training cluster is to design it around four layers:

1. Compute
2. Networking
3. Storage
4. Orchestration / software stack

Here’s a practical blueprint.

1) Compute

For LLM training, GPU choice matters most.

• Preferred GPUs: NVIDIA H100 / H200, A100, or B200-class if available
• Memory: More VRAM reduces sharding overhead and improves batch sizing
• Interconnect support: NVLink / NVSwitch on-node is very valuable
• CPU: Enough to feed GPUs, but not usually the bottleneck
• RAM: 2–4x GPU memory per node is a reasonable starting point

A common pattern:

• 8-GPU servers
• Dual-socket CPUs
• 512 GB to 1 TB system RAM
• Local NVMe scratch space

2) Networking

This is critical for multi-node training.

• At least 200 Gbps networking per node is a strong target
• InfiniBand is often preferred for large distributed jobs
• RDMA support can significantly reduce communication overhead
• Keep network topology simple and non-blocking if possible
• Use separate networks for:
  • training traffic
  • storage traffic
  • management traffic

If you’re doing tensor/pipeline parallelism across nodes, network quality becomes a major determinant of scaling efficiency.

3) Storage

You’ll want both fast local and shared storage.

Local

• NVMe SSDs for:
  • datasets cached locally
  • checkpoints
  • temporary shuffle files
• Helps reduce pressure on shared storage

Shared

• Parallel filesystem or high-performance object storage
• Common options:
  • Lustre
  • BeeGFS
  • Ceph (depending on performance goals)
  • S3-compatible object store for dataset/checkpoint archival

You should plan for:

• high read throughput for dataset streaming
• reliable checkpoint writing
• lifecycle management for old checkpoints

4) Software stack

Common pieces:

• OS: Ubuntu LTS or Rocky Linux
• Drivers: CUDA, cuDNN, NCCL
• Framework: PyTorch is the most common
• Distributed training:
  • FSDP
  • DeepSpeed
  • Megatron-LM
  • Tensor parallel + pipeline parallel + data parallel combinations
• Job scheduler:
  • Slurm is very common in HPC-style clusters
  • Kubernetes is possible, but less common for peak training performance unless carefully tuned

5) Cluster design recommendations

If you’re starting small

A practical first cluster:

• 2–4 nodes
• 8 GPUs each
• 200 Gbps networking
• Local NVMe
• Shared dataset storage
• Slurm for scheduling

If you’re scaling up

Design for:

• non-blocking fabric
• fault tolerance
• checkpointing every N minutes or N steps
• node failure recovery
• observability from day one

6) Operational concerns

Don’t skip these:

• Cooling and power
  • GPU clusters consume a lot of power
  • Validate rack density and air/liquid cooling capacity
• Cable management
  • Especially important with high-speed networking
• Monitoring
  • GPU utilization, memory, ECC errors, thermals, network congestion
• Security
  • Access controls for data and models
  • Secrets management for credentials
• Reproducibility
  • Containerize training environments
  • Pin driver/library versions

7) Training strategy

For large models, the architecture often matters as much as the hardware:

• Use mixed precision (BF16/FP16)
• Use gradient accumulation when batch size is constrained
• Use activation checkpointing
• Use FSDP or ZeRO for memory efficiency
• Choose parallelism strategy based on model size and node count

8) What to decide first

If you want to make good choices quickly, answer these:

1. Target model size?
2. Number of training nodes?
3. Budget?
4. On-prem or cloud?
5. Single-site or multi-site?
6. Training only, or inference too?
7. Need for compliance/security constraints?

If you want, I can help you with one of these next:

• a reference architecture
• a bill of materials
• a network topology
• a software stack
• a cost estimate
• a cluster sizing plan based on model size and budget