GPU Memory Usage: <500 MB

Learning Objectives

By the end of this section, you will:

1. Understand AMNL's memory footprint of under 500 MB
2. Analyze memory allocation across model components
3. Calculate memory scaling with batch size
4. Learn memory optimization techniques for constrained environments
5. Evaluate deployment options based on available memory

Core Insight: AMNL requires less than 500 MB of GPU memory for inference, enabling deployment on entry-level GPUs, edge devices, and even CPU-only systems. This low memory footprint makes predictive maintenance accessible to facilities without expensive hardware infrastructure.

Memory Footprint Analysis

GPU memory usage is a critical constraint for industrial deployment, where dedicated AI hardware may not be available.

Inference Memory Requirements

Memory Composition

Memory Breakdown by Component

Understanding where memory is allocated helps identify optimization opportunities.

Memory by Layer Type

Memory Scaling with Batch Size

Memory usage scales approximately linearly with batch size due to activation storage.

Scaling Formula

Where $B$ is the batch size. The 14 MB base is model weights, and 1.8 MB per sample is activation memory.

Memory vs Batch Size

Memory Optimization Techniques

Several techniques can reduce memory usage for constrained environments.

1. FP16 Inference

Half-precision inference halves both weight and activation memory:

1# Convert model to FP16
2model = model.half()
3
4# Memory reduction
5# FP32: 480 MB → FP16: 240 MB (batch=256)
6# Speedup bonus: ~1.5x faster

2. Gradient Checkpointing (Training)

For training, gradient checkpointing trades compute for memory:

1from torch.utils.checkpoint import checkpoint
2
3class MemoryEfficientAMNL(nn.Module):
4    def forward(self, x):
5        # Checkpoint BiLSTM to save memory
6        x = checkpoint(self.bilstm, x)
7        x = checkpoint(self.attention, x)
8        return self.heads(x)
9
10# Memory reduction: ~40% during training

3. Dynamic Batching

Adjust batch size based on available memory at runtime:

1def get_optimal_batch_size(available_memory_mb):
2    """Calculate optimal batch size given available GPU memory."""
3    base_memory = 14  # Model weights
4    per_sample = 1.8  # Activation memory per sample
5
6    max_batch = int((available_memory_mb - base_memory) / per_sample)
7
8    # Round down to power of 2 for efficiency
9    optimal = 2 ** int(np.log2(max_batch))
10
11    return min(optimal, 256)  # Cap at 256
12
13# Example: 2GB GPU
14# get_optimal_batch_size(2000) → 128

4. CPU Fallback

For edge devices without GPU, AMNL can run entirely on CPU with acceptable latency:

Summary

GPU Memory Usage - Summary:

1. Base memory: 480 MB at batch size 256 (FP32)
2. Activations dominate: 87.5% of memory is activations, not weights
3. Linear scaling: ~1.8 MB per sample in batch
4. FP16 halves memory: 240 MB at batch size 256
5. Edge deployment: Possible on devices with 500+ MB RAM

Key Insight: AMNL's memory footprint of under 500 MB enables deployment across a wide range of hardware—from high-end data center GPUs to edge devices. This democratizes predictive maintenance, allowing facilities of all sizes to implement AI-powered monitoring without significant infrastructure investment. The key to this efficiency is the compact 3.5M parameter design and the option for FP16 inference.