How to Install and Scale NVIDIA NeMo on Bare Metal

1. Step-by-Step Installation: Deploying NeMo on Bare Metal

For enterprise deployments, using the official NVIDIA NeMo Docker Container is highly recommended. It isolates the CUDA userspace and ships with preconfigured, optimized packages like TransformerEngine and DeepEP.

Step 1: Install Host Prerequisites (Docker CE)

Ensure your host machine has the appropriate NVIDIA Data Center drivers installed and verified via nvidia-smi.

# Add Docker's official GPG key and repository, then install Docker CE
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker $USER

Step 2: Install the NVIDIA Container Toolkit

The toolkit allows Docker to access the physical GPUs on your bare-metal server, acting as the bridge between the container and the NVIDIA drivers.

# Add the NVIDIA GPG Key and Repository
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# Install the toolkit
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

# Configure Docker runtime to use NVIDIA natively and restart
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Step 3: Pull the NeMo Container

Download the official NeMo Framework container from the NVIDIA NGC registry.

docker pull nvcr.io/nvidia/nemo:24.07

Step 4: Run NeMo with GPU Support

docker run --gpus all -it --rm \
  --ipc=host \
  --network=host \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  -v /mnt/nvme_storage/checkpoints:/workspace/checkpoints \
  -v /mnt/nvme_storage/datasets:/workspace/datasets \
  nvcr.io/nvidia/nemo:24.07

(Once inside the container, your bare-metal environment is primed for unthrottled NeMo multi-GPU orchestration).

2. Demystifying NVIDIA NeMo: The Architecture

NVIDIA NeMo is not just a workflow orchestrator; it is an integrated framework ecosystem for building, customizing, training, evaluating, and aligning large-scale AI models. Built on PyTorch, NeMo’s true power lies in its modular and decoupled architecture. The broader NeMo ecosystem is modular, with components such as NeMo Curator, NeMo Megatron-Bridge, NeMo AutoModel, and NeMo RL addressing different stages of model development and training.

• NVIDIA NeMo (The Framework): Brings together model-specific workflows, data preparation components, distributed training integrations, checkpointing, and post-training capabilities.
• Megatron-Core (The Distributed Training Engine): Provides optimized, composable PyTorch building blocks for large-scale transformer training. It implements parallelism strategies such as tensor, pipeline, and context parallelism, along with optimized communication and compute operations.

3. Megatron-Core and 3D Parallelism (How to Scale)

Full-scale training of a 70B-parameter model generally requires distributed GPU resources because model parameters, gradients, optimizer states, and activations create a substantially larger memory footprint than the weights alone. NeMo utilizes Megatron-Core to orchestrate a 3D Parallelism strategy:

• Tensor Parallelism (TP): Splits individual weight matrices and selected transformer computations across multiple GPUs. Because it involves frequent communication collectives, TP is typically placed within a high-bandwidth, low-latency GPU interconnect domain, such as an NVLink-connected node.
• Pipeline Parallelism (PP): Divides transformer layers into sequential stages distributed across GPUs. Stages can reside within one server or across multiple nodes, with activations transferred between stages over the available GPU interconnect or scale-out network.
• Data Parallelism (DP), Distributed Optimizer & FSDP: Megatron-Core supports multiple data-parallel strategies depending on the training architecture. Its Distributed Optimizer shards optimizer states across data-parallel ranks, reducing optimizer-state memory consumption while retaining the model parameter layout required by the selected TP and PP configuration. Sequence Parallelism (SP) complements Tensor Parallelism by distributing selected activations across participating GPUs. For workloads requiring more extensive model-state sharding, Megatron-FSDP can shard parameters, gradients, and optimizer states across data-parallel ranks. The appropriate strategy depends on model architecture, parallelism configuration, memory requirements, and software version.

4. SRE Strategies for Eradicating OOM Errors

Even with 80GB or 141GB of VRAM, improper configurations will cause Out-of-Memory (OOM) crashes. SREs rely on these targeted strategies within NeMo:

• Context Parallelism (CP): Distributes sequence-length dimensions and associated activations across multiple GPUs, reducing the per-GPU memory footprint during long-context training. Implementations can use communication strategies such as ring-based exchanges to make the required attention information available across GPUs. CP improves the ability to train with long sequences, but it does not eliminate the overall computational complexity of attention.
• Activation Checkpointing (Recomputation): Instead of retaining every intermediate activation from the forward pass, the training configuration selectively stores checkpoints and recomputes other activations during backpropagation. This reduces activation memory consumption at the cost of additional computation. The exact configuration options depend on the NeMo release, model implementation, and Megatron-Core recipe.
• Micro-Batch Tuning: Reducing the micro_batch_size per GPU lowers activation memory. The global batch size can be maintained through gradient accumulation. However, increasing gradient accumulation steps can preserve the target global batch size while reducing per-step activation memory, but it may affect optimizer update frequency, pipeline utilization, communication overlap, and overall training throughput.

5. RLHF Fine-Tuning: The Version-Sensitive Memory Trap

PPO-based RLHF can involve four major model roles: a trainable Actor, a frozen Reference model, a trainable Critic, and a Reward model. Depending on the implementation, these components may require substantial GPU memory, although their placement, residency, and execution schedules vary across training architectures.

6. Virtualization and Bare-Metal Performance Considerations

Virtualized GPU environments can introduce additional overhead depending on the hypervisor, GPU passthrough or vGPU configuration, CPU scheduling, storage path, and network architecture. The performance impact is workload- and platform-specific; there is no universal virtualization performance penalty.

For distributed training, AllReduce and other collective operations are sensitive to network latency, bandwidth, congestion, and communication consistency. Virtualized networking can introduce additional overhead in some configurations, while properly engineered RDMA and passthrough deployments can minimize much of that overhead. Consequently, architects should benchmark the actual GPU, NIC, and network configuration.

Bare-Metal Infrastructure for Large-Scale AI Training

Bare-metal deployment provides direct access to the physical server and avoids the hypervisor layer for the host workload. Dedicated bare-metal servers provide single-tenant access to the allocated physical server and its installed hardware. Where the selected configuration includes compatible GPUs, NVLink connectivity, PCIe peer-to-peer support, and RDMA-capable NICs, customers can build optimized GPU communication paths without sharing the underlying host resources with other tenants.

Conclusion

NVIDIA NeMo provides an integrated framework ecosystem for developing, training, and aligning large-scale generative AI models. By combining NeMo's model workflows with Megatron-Core's distributed training capabilities, organizations can scale transformer workloads across multi-GPU and multi-node infrastructure.

However, successful large-scale training depends on more than the framework itself. GPU memory management, tensor and pipeline parallelism, activation recomputation, storage throughput, and high-bandwidth interconnects all contribute to training efficiency.

For organizations building dedicated AI infrastructure, bare-metal servers provide direct access to physical GPU, PCIe, storage, and networking resources, creating a flexible foundation for performance tuning and infrastructure isolation. When combined with compatible GPUDirect technologies and a properly engineered network fabric, this architecture can support demanding LLM pretraining and post-training workloads.