A GPU node is just a specialized compute machine on the cloud or in a server rack built to crunch AI workloads fast.
It contains CPU → GPU → Memory → Storage → Networking pieces wired together to accelerate training & inference.
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CPU (vCPUs)
Prepares batches for the GPU
Handles orchestration logic
Runs the OS, Docker, Kubernetes kubelet, Triton server wrapper, etc.
GPU(s)
Runs deep learning calculations (matrix multiplies)
Performs training + inference
Uses CUDA, cuDNN, TensorRT, NCCL
System RAM
Stores data before GPU receives it
Holds CPU-side preprocessed batches
If this is too low → bottlenecks
GPU VRAM
Memory the GPU directly reads
Holds model weights + activations + tensors
Low VRAM → out-of-memory errors
Local NVMe Storage
Very fast SSD
Used for:
Caching datasets
Model files
Temporary training artifacts
Logging
Networking
Needed for:
Distributed training
Fetching data from S3/GCS
Model serving traffic
K8s cluster communication
GPU nodes support 100 Gbps or GPU-direct networking
Key Takeway:
Even though a GPU is the fastest part of the system, it can’t reach peak performance unless CPU, RAM, network, and storage keep feeding it data fast enough. When any of these are slow, the GPU sits idle a problem known as GPU starvation.
This is the #1 hidden bottleneck in AI training and inference clusters.
The solution? Optimize the whole pipeline, not just the GPU.
