Core Compute Kernels: MLP, Softmax, LayerNorm, and Memory Management

Generative AI models rely heavily on a small set of core operations, or compute kernels, that handle most of the computation in modern transformers. Understanding these kernels and how they interact with hardware like GPUs and accelerators is key to improving performance and efficiency.

Multi-Layer Perceptron (MLP)

Inside every transformer block is a feedforward network, commonly referred to as an MLP. In simple terms, this is a two-layer network with an activation function in between, responsible for transforming features from one representation to another.

• Role in GenAI models: MLP layers process token embeddings to capture complex patterns and interactions.
• Why it matters for hardware: MLP operations are dominated by matrix multiplications, which are extremely compute-intensive. Optimizing them for GPUs using fused kernels and mixed-precision arithmetic can drastically improve performance and reduce memory overhead.

How it’s performed:

• Essentially a sequence of matrix multiplications followed by activation functions (e.g., GELU, ReLU).
• For transformers, MLPs handle the dense transformations between attention layers.
• On GPUs, these are executed as batched GEMM (General Matrix Multiply) operations to maximize parallelism.

Open-source tools:

• PyTorch / TensorFlow: Provide highly optimized Linear layers that handle MLPs efficiently.
• cuBLAS / CUTLASS: NVIDIA libraries for GPU-accelerated matrix multiplication.
• DeepSpeed & FasterTransformer: Libraries that fuse MLP and activation operations for performance.

Softmax

Softmax is the mathematical operation that converts scores into probabilities. In transformers, it’s primarily used in attention mechanisms, helping the model decide how much focus to give each token in the input sequence.

• Role in GenAI models: Softmax normalizes attention scores so the model can weigh relationships between tokens effectively.
• Why it matters for hardware: Softmax involves element-wise operations and reductions, which can be memory-bound. Optimizing it with vectorized operations and fused attention kernels ensures GPUs are fully utilized without bottlenecks.

How it’s performed:

• Computes Softmax​​ across a vector.
• Requires exponentiation, sum reduction, and division, which are parallelized on GPUs.
• For large batches, often implemented in fused kernels to avoid multiple memory reads/writes.

Open-source tools:

• PyTorch / TensorFlow: Standard softmax layers; can leverage torch.nn.functional.softmax.
• Fused kernels in Apex / Triton: Reduce memory overhead for large-scale transformers.
• ONNX Runtime: Optimized softmax for inference.

Layer Normalization (LayerNorm)

LayerNorm stabilizes the learning process by normalizing inputs across features for each token. It helps transformers train faster and maintain numerical stability, particularly in deep networks.

• Role in GenAI models: Ensures activations remain in a stable range, improving convergence and generalization.
• Why it matters for hardware: While less computationally heavy than MLPs, LayerNorm can create memory access bottlenecks. Combining it with residual connections and bias operations in a single fused kernel reduces memory traffic and speeds up execution.

How it’s performed:

• Normalizes inputs across features
• Involves mean and variance computation, elementwise operations, and optional learnable scale/bias.
• Often fused with preceding operations to reduce memory traffic.

Open-source tools:

• PyTorch / TensorFlow: LayerNorm layers with GPU support.
• NVIDIA Apex / Triton: Fused LayerNorm implementations for speed.
• DeepSpeed / HuggingFace Transformers: Optimized for large models.

Memory Management

Transformers are memory-intensive. During inference, models store activations, attention caches, and intermediate states for every token and every layer. For large sequences or batch sizes, memory usage can skyrocket.

• Role in GenAI models: Efficient memory management ensures that models can handle long sequences and large batch sizes without exceeding GPU memory limits.
• Why it matters for hardware: Techniques like flash attention, recomputation, and careful buffer allocation reduce memory footprint and prevent slowdowns caused by spilling to host memory.

How it’s performed:

• AI models generate huge intermediate tensors during forward/backward passes.
• Efficient memory usage involves reusing buffers, offloading to CPU or disk, and mixed-precision computation.

Open-source tools:

• PyTorch: torch.cuda.memory_allocated & torch.cuda.empty_cache for manual control.
• DeepSpeed: ZeRO optimizer shards model states across GPUs to reduce memory load.
• PyTorch + Accelerate: Manages distributed GPU memory automatically.
• FlashAttention / xFormers: Memory-efficient attention implementations.

Why Hardware Optimization is Critical

GPUs, TPUs, and other accelerators are incredibly fast, but they have finite memory and bandwidth. Poorly optimized kernels can lead to:

• Underutilized compute units
• Memory bottlenecks
• Slower inference and training times
• Increased cost for cloud-based training

Optimizing kernels — from MLP and Softmax to LayerNorm and memory handling — ensures that every watt of GPU power is effectively used, enabling faster and more cost-efficient GenAI workloads.

Compute Kernel	Function in GenAI Models	Hardware Optimization Considerations
MLP (Multi-Layer Perceptron)	Performs dense feedforward computations between layers; applies learned weights and biases.	Optimize matrix multiplications for GPU tensor cores; batch operations to reduce memory access overhead.
Softmax	Converts raw scores (logits) into probabilities across classes.	Use fused GPU kernels to reduce latency; minimize precision loss with mixed-precision computation.
LayerNorm	Normalizes activations across a layer to stabilize training and improve convergence.	Fuse normalization with preceding operations; optimize memory access patterns to reduce bandwidth bottlenecks.
Memory Management	Handles storage and retrieval of intermediate activations and model weights.	Efficient memory allocation and reuse; minimize data transfer between GPU and CPU; leverage hardware caches.

Summary

While GenAI models often get attention for their size and architecture, the real work happens at the kernel level. MLP layers, Softmax, LayerNorm, and memory management dominate compute time, and optimizing them for hardware is what separates practical, scalable AI from expensive experiments.

For practitioners and engineers, understanding these kernels is key to designing efficient AI stacks that can deliver high performance without breaking the bank.