Computation and Hardware Architecture

Hardware Architecture

GPU Components

• Parallel Processing Units:
  • Specialized for matrix operations, enabling thousands of simultaneous calculations
  • CUDA cores for general compute
  • RT cores for ray tracing (useful in some AI visualization tasks)
• Tensor Cores:
  • Hardware accelerators designed specifically for AI workloads
  • Up to 8x speedup for matrix operations
  • Generational improvements (Ampere, Ada Lovelace architectures)
• Memory Hierarchy:
  • High Bandwidth Memory (HBM): Ultra-fast main GPU memory (up to 2TB/s)
  • L2 Cache: Shared intermediate storage (up to 96MB in modern GPUs)
  • Shared Memory: Fast per-block memory (configurable with L1 cache)
  • Register File: Fastest, per-thread storage
• Memory Bandwidth:
  • Critical for model performance, typically 1-2TB/s in modern GPUs
  • PCIe bandwidth considerations for multi-GPU setups
  • NVLink for high-speed GPU-to-GPU communication

CPU vs GPU Considerations

• CPUs excel at:
  • Sequential tasks and complex logic
  • Dynamic control flow
  • System management and I/O
  • Small batch inference
• GPUs optimal for:
  • Parallel matrix operations
  • Large batch processing
  • Regular computation patterns
  • High throughput inference
• Hybrid approaches often yield best results:
  • CPU for preprocessing and orchestration
  • GPU for model computation
  • Balanced memory management
  • Efficient data transfer strategies

Workload Types and Requirements

Inference

• Lower memory requirements than training
• Emphasis on latency and throughput
• Supports lower precision (FP16, INT8) with minimal accuracy loss
• Optimization techniques:
  • Batching to maximize throughput
  • Dynamic batch sizing
  • Kernel fusion
  • Attention caching
• Key metrics:
  • Requests/second
  • Latency percentiles
  • Memory utilization
  • Cost per inference

Fine-tuning

• Moderate memory requirements
• Higher precision needs (FP32) for stable training
• Distributed training capable
• Memory optimization via gradient accumulation
• Important factors:
  • Dataset size and quality
  • Learning rate scheduling
  • Batch size optimization
  • Checkpoint strategy
  • Validation frequency

Pre-training

• Highest resource demands
• Requires distributed infrastructure
• Significant storage needs for datasets
• Long-running workloads (weeks to months)
• Critical considerations:
  • Checkpoint management
  • Fault tolerance
  • Data pipeline efficiency
  • Distributed training strategy
  • Cost optimization

Resource Requirements

GPU Memory Estimation

For quick estimation of GPU requirements:

Inference

\[ \text{Number of GPUs} \approx \frac{\text{model\_parameters (billions)} \times \text{precision (bytes)}}{\text{gpu\_memory (GB)}} \]

Training

\[ \text{Number of GPUs} \approx 6 \times \frac{\text{model\_parameters (billions)} \times \text{precision (bytes)}}{\text{gpu\_memory (GB)}} \]

Key Parameters:

• precision typically:
  • FP32 (4 bytes): Higher accuracy, training
  • FP16 (2 bytes): Balanced performance/accuracy
  • INT8 (1 byte): High-performance inference
  • Mixed precision: Combines multiple formats
• Training multiplier (~6x) accounts for:
  • Optimizer states (2x)
  • Gradients (1x)
  • Forward activations (1x)
  • Temporary buffers (2x)
• Additional considerations:
  • Batch size impacts memory linearly
  • Attention mechanisms scale quadratically with sequence length
  • Framework overhead varies (PyTorch, TensorFlow, etc.)
  • Memory fragmentation overhead

Optimization Strategies

Memory Optimization

• Model Quantization:
  • Reduces precision while maintaining accuracy
  • Common formats: FP16, BF16, INT8
  • Post-training vs. quantization-aware training
  • Calibration techniques for optimal accuracy
• Gradient Accumulation:
  • Splits large batches into micro-batches
  • Trades speed for memory efficiency
  • Enables larger effective batch sizes
  • Helps with limited GPU memory
• Model Sharding:
  • Distributes model across devices
  • Zero Redundancy Optimizer (ZeRO) stages
  • Tensor parallelism strategies
  • Pipeline parallelism options
• KV Cache Management:
  • Crucial for transformer inference
  • Sliding window approaches
  • Structured state pruning
  • Dynamic allocation strategies

Compute Optimization

• Batching Strategies:
  • Dynamic batching for varied input sizes
  • Automatic batch size selection
  • Priority-based scheduling
  • Token-based batching
• Mixed Precision Training:
  • FP16/BF16 computation with FP32 master weights
  • Automatic loss scaling
  • Gradient clipping strategies
  • Stability monitoring
• Parallel Processing:
  • Tensor Parallelism: splits individual tensors
  • Pipeline Parallelism: splits model layers
  • Data Parallelism: splits batch processing
  • Hybrid approaches for optimal scaling
  • Communication optimization

Hardware-Specific Optimization

• GPU Architecture Considerations:
  • SM occupancy optimization
  • Memory coalescing
  • Warp efficiency
  • Kernel fusion opportunities
• Multi-GPU Strategies:
  • NVLink utilization
  • PCIe bandwidth management
  • Host-device transfer optimization
  • NUMA considerations
• CPU Offloading:
  • Preprocessing optimization
  • I/O management
  • Memory transfers
  • System coordination

GPU Selection Guide

Comprehensive analysis of GPU options for different AI workloads.