Table of contents
Why multi-GPU?Scaling strategiesHardware considerationsSoftware and orchestration stackBest practices and tipsWhen you need a multi-GPUHow to choose a hosting providerNext stepsAdditional resources
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GPU → Multi-GPU Setups

Multi-GPU setups for large model training: Scaling strategies and hardware considerations

Scaling has always been a challenge in the IT industry, and now, as deep learning models grow beyond the capacity of a single GPU, training them efficiently will require horizontal scaling where we add more GPUs and distribute the workload across them.

Whether the goal is fine-tuning LLaMA-3, pretraining a custom vision-language model, or iterating on your own foundation model, multi-GPU setups are no longer a luxury—they’re a necessity.

Let’s walk through the key strategies for scaling across GPUs and the critical hardware choices that can make or break your training efficiency.

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Why scale training across multiple GPUs?

Growing size of modern ML models

Large-scale models are no longer just the domain of OpenAI and Meta. Open-source models like Mistral, Stable Diffusion, and Gemma have billions of parameters and require tens to hundreds of gigabytes of VRAM to train effectively.

Batch sizes are ballooning, and datasets are often measured in terabytes or petabytes. Trying to run all that on a single GPU is likely to hit performance bottlenecks.

Limitations of a single GPU

Even with 48–80GB VRAM on single, high-end cards like the H100 or A100, you’ll hit performance ceilings fast:

  • Limits on VRAM – The dedicated memory on a GPU prevents large batch sizes.
  • PCIe bandwidth becomes a bottleneck for host-device transfers.
  • You can’t effectively parallelize your workload or reduce training time without distribution.

Scaling across GPUs is the only practical path forward for serious model training.

Scaling strategies: data, model, and pipeline parallelism

Next, let’s consider the primary techniques for distributing model training workloads across multiple GPUs—data parallelism, model parallelism, pipeline parallelism, and hybrid approaches—along with when and why to use each.

Data parallelism

This is the most straightforward and common strategy due to its simplicity: you replicate the model on each GPU and split the input data across them. Each worker performs IO independently at the start of each batch. Gradients are averaged using an all-reduce operation.

  • Pros: Easy to implement, well-supported in frameworks like PyTorch and TensorFlow
  • Cons: Doesn’t help with very large models that don’t fit on one GPU
  • Best for: Mid-sized models with large datasets

Model parallelism

Here, the model itself is split across multiple GPUs—layer by layer or tensor by tensor.

  • Pros: Allows training models too large for a single GPU
  • Cons: Requires careful placement and communication management
  • Best for: Massive models like GPT-3, where even one layer doesn’t fit in VRAM

Pipeline parallelism

The model is divided into sequential stages. Each GPU processes a mini-batch stage-by-stage in a pipeline. Performance will depend on load balancing.

  • Pros: Reduces peak memory usage
  • Cons: Introduces pipeline bubbles (idle time), adds latency
  • Best for: Training large models with tight memory constraints and predictable batch flow

Hybrid parallelism

State-of-the-art training frameworks like Megatron-LM, DeepSpeed, and MosaicML combine multiple strategies: data parallelism for scalability, model parallelism for size, and pipeline parallelism for memory efficiency.

Tradeoff: Powerful but complex to implement and debug

Hardware considerations for multi-GPU training

Key hardware factors impact multi-GPU training performance, including GPU interconnects, server architecture, GPU model selection, and cooling and power requirements.

Interconnects: PCIe vs NVLink vs InfiniBand

  • PCIe is common but can bottleneck peer-to-peer GPU communication
  • NVLink (NVIDIA proprietary) allows fast direct memory access between GPUs on the same node
  • InfiniBand enables ultra-low latency, high throughput between nodes—critical for distributed training

Server types: single-node vs multi-node

  • Single-node GPU servers can house 2–8 GPUs connected via NVLink, ideal for most use cases
  • Multi-node clusters rely on InfiniBand or high-speed Ethernet for distributed workloads and are used for hyperscale training

GPU selection

Consider the following specs for your workload:

  • H100: Flagship for FP8/FP16, Transformer Engine support, NVLink 4, best for LLMs
  • L40S: Optimized for inferencing and AI graphics workloads

Cooling and power

  • Each high-end GPU can draw 300–700W+
  • Multi-GPU setups may require 2–3kW per server rack
  • Consider liquid cooling or high-efficiency airflow designs for dense configurations

An alternative here is to partner with a hosting provider who houses, manages, and secures your GPU hardware for you.

Software and orchestration stack

Let’s talk about the software frameworks, orchestration tools, and best practices needed to coordinate multi-GPU training workloads effectively across single-node or distributed systems.

Framework-level distribution

  • PyTorch: torch.distributed, torchrun, and FSDP for large-scale training
  • TensorFlow: MultiWorkerMirroredStrategy for multi-GPU and multi-node setups
  • JAX: Scales via pmap (single-node, multi-device) or pjit/xmap for distributed settings with jax.distributed.
  • Horovod: All-reduce optimized backend that works with both TF and PyTorch
  • Hugging Face Accelerate: Great for bootstrapping multi-GPU with minimal code changes

Scheduling and orchestration

  • Slurm: Standard in HPC clusters for managing GPU jobs
  • Kubernetes + KubeFlow or Run:AI: Cloud-native job orchestration
  • Containers with Docker or Singularity 

Checkpointing and fault tolerance

  • Use sharded datasets with tools like DeepSpeed or FSDP
  • Leverage parallel dataloaders and prefetching to optimize input pipelines.
  • Resume logic is critical in multi-day runs—especially with spot instances or unstable clusters
  • Monitor GPU utilization using NVIDIA tools (nvidia-smi, Nsight).

Best practices and performance tips

To get the most out of your multi-GPU setup, it’s essential to apply performance tuning strategies that reduce training time, improve hardware utilization, and maintain model accuracy.

Batch size tuning and gradient accumulation

Train large models efficiently within GPU memory limits while maintaining high throughput and stable convergence.

  • Large batch sizes improve throughput but may cause convergence issues.
  • Use gradient accumulation to simulate large batches when VRAM is tight.

Mixed precision training

Mixed precision training speeds up model training and reduces memory usage.

  • FP16 and BF16 reduce memory use and increase speed.
  • Use NVIDIA’s Automatic Mixed Precision (AMP) or Transformer Engine on H100s.

Profiling, monitoring, and bottleneck diagnosis

Identify performance issues—like underutilized GPUs or slow data transfers—so you can optimize training speed and resource usage.

  • Tools: NVIDIA Nsight, PyTorch Profiler, TensorBoard
  • Identify slow kernels, communication delays, or underutilized GPUs.

When to upgrade to a multi-GPU dedicated server

If you’re currently training models on a single GPU, or relying on GPU-as-a-Service platforms, you’ll eventually hit a wall. Whether it’s compute bottlenecks, escalating costs, or inflexible environments, the limitations become obvious as your models and datasets grow.

You’re likely ready for a multi-GPU dedicated server if:

  • You’re training models that exceed 24–48GB of VRAM and can’t be easily split across smaller GPUs.
  • Your training time per epoch is measured in hours or days, and time-to-market is becoming a bottleneck.
  • You need full control over the software stack, including OS, drivers, CUDA versions, and custom compilers.
  • You’re running multiple experiments concurrently, such as hyperparameter sweeps or multi-model fine-tuning.
  • You want predictable long-term costs, without pay-as-you-go variance or resource contention.

Upgrading to a multi-GPU server means unlocking the ability to scale your workflows efficiently without sacrificing control or reliability.

How to choose a multi-GPU server hosting provider

Not all GPU servers or providers are created equal. The wrong setup can throttle performance, create instability, or lock you into inflexible infrastructure. When evaluating providers for multi-GPU training, prioritize these criteria:

  • Full GPU access: Ensure the server provides full, dedicated access to the GPUs (not virtualized slices).
  • High-bandwidth interconnects: Look for NVLink or PCIe Gen4+ for intra-node communication. InfiniBand is a must for multi-node clustering.
  • Modern GPU options: These would be better suited for servers with NVIDIA H100 or L40S—depending on your workload and budget.
  • Cooling and power infrastructure: Multi-GPU workloads generate serious heat. Choose a provider with proven rack-level thermal and power management.
  • Customization flexibility: You should be able to choose your OS, install custom drivers, and deploy your preferred orchestration stack.
  • Support and management tiers: Depending on your team’s expertise, you may want a provider who offers optional server management, monitoring, and fast-response hardware support.

A good provider acts like an infrastructure partner, not just a vendor. They’ll help you scale intelligently, deploy fast, and keep your model training on schedule.

Additional resources

What is a GPU? →

A complete beginner’s guide to GPUs and GPU hosting

Best GPU server hosting [2025] →

Top 4 GPU hosting providers side-by-side so you can decide which is best for you

A100 vs H100 vs L40S →

A simple side-by-side comparison of different NVIDIA GPUs and how to decide

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Amy Moruzzi is a Systems Engineer at Liquid Web with years of experience maintaining large fleets of servers in a wide variety of areas—including system management, deployment, maintenance, clustering, virtualization, and application level support. She specializes in Linux, but has experience working across the entire stack. Amy also enjoys creating software and tools to automate processes and make customers’ lives easier.