Custom CUDA Kernels via Triton for AI Workloads

When Custom Kernels Pay Off

Stock PyTorch ops are optimized but generic. For specific patterns — fused attention, custom activations, sparse operations — custom CUDA kernels can deliver 2-10x speedups. Writing CUDA in C++ is hard; Triton makes it tractable.

By 2026 Triton is the standard tool for performance-engineering teams writing custom GPU kernels for AI.

What Triton Is

flowchart LR
    PyT[Python with Triton DSL] --> Compile[Triton compiler]
    Compile --> PTX[PTX/CUDA]
    PTX --> GPU[Run on GPU]

Triton is a Python DSL for writing GPU kernels. Decorators mark Triton functions; the compiler emits optimized GPU code. The developer reasons about blocks of work, not threads.

When You Need It

• Operations PyTorch does not have natively
• Fusion opportunities the compiler does not catch
• Sparse / structured operations
• Quantized operations
• Mixed-precision custom ops

For most teams, Flash Attention 3 is already integrated; you do not need to write it. You write Triton kernels for the long tail of operations.

A Pattern: Fused Operations

Instead of three separate kernels (matmul, add bias, ReLU), one fused kernel reads inputs once, writes outputs once. Memory bandwidth is the bottleneck; fusion saves it.

flowchart LR
    Sep[Separate kernels: 3 round trips to memory] --> Slow[Slow]
    Fused[Fused kernel: 1 round trip] --> Fast[Fast]

For attention, this is what Flash Attention does. For other ops, custom Triton kernels can match or beat stock ops by 2-3x.

What Production Teams Ship

In 2026 production codebases:

• Custom rotary embedding kernels for LLM serving
• Custom quantization kernels for mixed-precision
• Custom mask handling for sparse attention
• Custom embedding lookup with batched index

Each of these has stock implementations; the custom versions ship when the team has measured a real bottleneck.

When NOT to Write Custom Kernels

• Standard transformer ops (Flash Attention, GQA) are already optimized
• Small workloads where kernel overhead exceeds savings
• One-off prototypes

Most application-level teams should not write Triton. Performance engineering teams should.

The Trade-Off

• Speedup: 2-10x on the targeted op
• Cost: engineering effort (days to weeks per kernel)
• Maintenance: kernel must be re-tuned for new GPU architectures
• Risk: subtle bugs that produce numerically wrong outputs

For high-volume training and inference, the speedup pays back. For one-off scripts, never.

Tooling

• Triton: the DSL itself
• Triton Inductor: PyTorch's compiler that uses Triton
• CUTLASS: NVIDIA's CUDA template library; harder but extreme performance
• CUDA C++: lowest-level option

Most 2026 teams write Triton; CUTLASS and CUDA are reserved for kernels that Triton cannot optimize.

Example Patterns

A simple Triton kernel for element-wise add looks like:

@triton.jit
def add_kernel(x_ptr, y_ptr, output_ptr, n):
    pid = tl.program_id(axis=0)
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n
    x = tl.load(x_ptr + offsets, mask=mask)
    y = tl.load(y_ptr + offsets, mask=mask)
    output = x + y
    tl.store(output_ptr + offsets, output, mask=mask)

Real production kernels are more elaborate but follow the same pattern.

Validating Correctness

Custom kernels can be subtly wrong. The discipline:

• Compare output to a stock PyTorch implementation on a wide range of inputs
• Test edge cases (sizes, dtypes, devices)
• Run gradient checks if backward pass is custom
• Stress-test under realistic workloads

A custom kernel without rigorous validation is a future incident.

Sources

• Triton documentation — https://triton-lang.org
• Triton Inductor — https://pytorch.org/blog
• Flash Attention source (Triton-based) — https://github.com/Dao-AILab/flash-attention
• CUTLASS — https://github.com/NVIDIA/cutlass
• "Triton tutorial" — https://triton-lang.org/main/getting-started/tutorials

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