Editing AI Chips and Hardware Accelerators (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Key AI Chip Comparison (as of 2024)
! Chip !! Peak FP16 TFLOPS !! Memory !! Bandwidth !! Best For
|-
| NVIDIA H100 SXM || 1,979 || 80GB HBM3 || 3.35 TB/s || Large model training, top inference
|-
| NVIDIA A100 SXM || 312 (TF32) || 80GB HBM2e || 2.0 TB/s || Training workhorse (prior gen)
|-
| NVIDIA RTX 4090 || 165 || 24GB GDDR6X || 1.0 TB/s || Consumer training, fine-tuning
|-
| Google TPU v4 || 275 || 32GB HBM || 1.2 TB/s || Large-scale training (TPU pods)
|-
| AMD MI300X || 1,307 || 192GB HBM3 || 5.3 TB/s || Memory-hungry inference, very large models
|-
| Groq LPU || 750 || 230MB SRAM (on-chip) || 80 TB/s (on-chip) || Ultra-low latency inference
|}

'''Failure modes and bottlenecks:'''
* '''VRAM OOM (Out of Memory)''' — The most common training failure. Activations, gradients, optimizer states, and model weights all compete for VRAM. Fix: reduce batch size, gradient checkpointing, activation offloading, or model parallelism.
* '''Low GPU utilization''' — Compute cores idle while waiting for data. Causes: slow data loading (increase DataLoader workers, use DALI), small batch sizes, communication overhead in multi-GPU. Use nvidia-smi or PyTorch profiler to diagnose.
* '''Communication bottleneck in distributed training''' — In multi-node training, gradient synchronization across nodes via InfiniBand can dominate training time. Fix: gradient compression, ZeRO-3 with CPU offload, reduce gradient sync frequency.
* '''Thermal throttling''' — GPUs reduce clock speed when temperature exceeds threshold. Relevant for long training runs without adequate cooling.
</div>

<div style="background-color: #483D8B; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">