Computation and Hardware Architecture

Hardware Architecture

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

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

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

For quick estimation of GPU requirements:

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

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

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

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

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

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

Comprehensive analysis of GPU options for different AI workloads.