Understanding GPU Scaling
Explore how GPU infrastructure scales from a single accelerator to multi-node datacenter deployments. Learn about the architectural patterns that enable modern AI workloads.
Scaling Patterns in Modern AI Infrastructure
Learn about different GPU deployment architectures from entry-level to datacenter scale
Single GPU Architecture
A single next-gen GPU accelerator represents the fundamental unit of modern AI compute. Learn how 192GB of HBM3e memory and 8TB/s bandwidth enable inference for models up to 70B parameters.
- Memory: 192GB HBM3e for large model weights
- Bandwidth: 8TB/s for rapid weight access
- Compute: 20 petaFLOPS FP4 throughput
- Use Cases: Inference, fine-tuning, research
Ideal for learning GPU fundamentals
Multi-GPU Scaling
When models exceed single-GPU memory, high-speed interconnects enable distributed training. Explore how 8 GPUs communicate at 1.8TB/s per link to synchronize gradients and share model weights.
- Topology: All-to-all GPU interconnect fabric
- Memory Pool: 1.5TB unified across GPUs
- Bandwidth: 14.4TB/s aggregate fabric
- Workloads: 70B-400B parameter models
Foundation of modern LLM training
Datacenter-Scale AI
Training trillion-parameter foundation models requires hundreds to thousands of GPUs. Learn how InfiniBand networks and advanced scheduling enable efficient utilization across racks.
- Scale: 16-256+ GPU multi-node clusters
- Network: InfiniBand spine-leaf topology
- Scheduling: Slurm for multi-tenant jobs
- Models: 400B-1T+ parameter training
Powers foundation model research
Understanding AI Workload Patterns
LLM Training Patterns
How model size determines GPU configuration needs. Understanding the relationship between parameters and memory:
- •7-70B models: Fit in single GPU memory (140GB at FP16)
- •70-400B models: Require model parallelism across 8+ GPUs
- •400B-1T models: Need data + model + pipeline parallelism
Inference Optimization
How GPU memory and compute enable real-time inference. Understanding throughput vs latency tradeoffs:
- •Small models (7-13B): Batch multiple requests for throughput
- •Medium models (33-70B): Balance batch size with latency
- •Large models (70B+): Tensor parallelism for single requests
Vision Workloads
Computer vision models have different GPU requirements than LLMs. Understanding spatial vs sequential processing:
- •CNNs: Compute-bound, benefit from tensor cores
- •Vision transformers: Memory-bound like LLMs
- •Diffusion models: Require repeated inference passes
Embedding-Heavy Models
Recommender systems and retrieval models have unique GPU needs. Understanding embedding table scaling:
- •Memory-bound: Embedding tables can exceed 100GB
- •Random access: HBM bandwidth critical for lookups
- •Hybrid systems: Combine GPUs with host memory
Understanding the Software Stack
GPU compute requires a complex software ecosystem. Learn about the layers from hardware drivers to ML frameworks.
Deep Learning Frameworks
- PyTorch 2.3+
- TensorFlow 2.16+
- JAX 0.4+
- ONNX Runtime
GPU Software Stack
- CUDA 12.4
- cuDNN 9.0
- Multi-GPU Communication Library 2.21
- TensorRT 10.0
Infrastructure Tools
- Docker + GPU Container Toolkit
- JupyterLab
- Slurm (Multi-GPU)
- Weights & Biases
Continue Your Learning Journey
This educational demonstration explores GPU scaling patterns from single accelerators to datacenter deployments. Part of the Global Knowledge Graph Network's mission to make AI infrastructure concepts accessible.