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Every few months, a new LLM appears claiming to be 2× faster, 3× cheaper, or capable of serving millions more tokens per second.
Many developers assume the gains come from better GPUs or smaller models.
Often, the real answer is far less glamorous:
Someone removed a few trips to memory.
One of the most important performance techniques in modern LLM inference is kernel fusion. It doesn't change the model architecture. It doesn't improve accuracy. It doesn't make the AI smarter.
It simply makes the hardware spend less time waiting and more time computing.
And in large-scale AI systems, that can mean the difference between serving thousands of users and serving millions.
Let's dig into how fused kernels work, starting from intuition and moving down to GPU-level details.
1. Why LLMs Are Often Memory-Bound
When developers first think about neural network performance, they usually focus on FLOPS.
Modern GPUs advertise enormous numbers:
- Tens of TFLOPS
- Hundreds of TFLOPS
- Even PFLOPS for specialized workloads
Yet many LLM operations don't come close to using that compute capacity.
The reason is that a GPU spends a surprising amount of time moving data around.
Imagine a simple operation:
y = gelu(x + bias)
Conceptually this is tiny.
But naively, the GPU may:
- Load
x - Load
bias - Compute addition
- Write result to memory
- Read result back
- Compute GELU
- Write final output
The arithmetic is cheap.
The memory traffic is expensive.
As models grow into billions of parameters, memory movement becomes one of the dominant costs.
2. What Is a GPU Kernel?
Before understanding fusion, we need to understand kernels.
A GPU kernel is essentially a program launched on the GPU.
For example:
z = x + y
might launch one kernel.
Then:
output = relu(z)
might launch another.
Then:
output = output * scale
might launch a third.
Each kernel launch has overhead:
- Scheduling work
- Reading memory
- Writing memory
- Synchronization
The GPU repeatedly moves intermediate results between global memory and compute units.
Those extra movements add up quickly.
3. The Core Idea of Kernel Fusion
Kernel fusion combines multiple operations into a single GPU kernel.
Instead of:
z = x + bias
a = gelu(z)
output = a * scale
we create one fused operation:
output = scale * gelu(x + bias)
Now the GPU can:
- Load input once
- Perform all calculations
- Write output once
No intermediate tensors are stored in global memory.
Visually:
Without fusion
Memory → Add
↓
Memory
↓
GELU
↓
Memory
↓
Scale
↓
Memory
With fusion
Memory → Add → GELU → Scale → Memory
The computation is identical.
The data movement is dramatically reduced.
4. Where Fusion Appears Inside LLMs
Modern transformers contain many opportunities for fusion.
A few common examples:
Bias + GELU Fusion
Instead of:
hidden = linear(x)
hidden += bias
hidden = gelu(hidden)
The bias addition and activation are fused.
This is common in transformer MLP blocks.
LayerNorm Fusion
Layer normalization requires:
- Mean computation
- Variance computation
- Normalization
- Scale
- Shift
Naively these can involve multiple passes through memory.
Optimized kernels perform much of the work in one fused operation.
Softmax Fusion
Attention layers require softmax:
softmax(QK^T)
Implementations often fuse:
- Scaling
- Masking
- Softmax
into a single kernel.
This reduces memory traffic significantly.
5. FlashAttention: The Famous Fusion Example
One of the best-known examples of fusion is Tri Dao's FlashAttention.
The traditional attention pipeline looks roughly like:
QK^T
↓
Store matrix
↓
Mask
↓
Store matrix
↓
Softmax
↓
Store matrix
↓
Multiply by V
The intermediate attention matrix can be enormous.
For long contexts it becomes a major bottleneck.
FlashAttention reorganizes the computation so that large intermediate matrices never need to be materialized in global memory.
Instead:
- Data is processed in tiles
- Shared memory is heavily used
- Multiple attention steps are effectively fused together
The result is dramatically lower memory usage and substantially higher throughput.
This single optimization helped unlock much longer context windows for modern LLMs.
6. How Fusion Works at the GPU Level
Let's go one level deeper.
Modern GPUs have a hierarchy:
Global Memory (HBM)
↓
L2 Cache
↓
Shared Memory
↓
Registers
Global memory is large but relatively slow.
Registers are extremely fast but tiny.
Fusion attempts to keep intermediate values as close to registers as possible.
Instead of:
Compute
↓
Write to HBM
↓
Read from HBM
↓
Compute
we get:
Compute
↓
Register
↓
Compute
↓
Register
↓
Compute
This drastically increases arithmetic intensity:
Useful Computation
------------------
Bytes Moved
Higher arithmetic intensity generally means better GPU utilization.
This is why fusion often produces large speedups even when the number of mathematical operations stays exactly the same.
7. Why Fused Kernels Are Hard to Build
If fusion is so beneficial, why not fuse everything?
Because fusion introduces complexity.
Several challenges emerge:
Register Pressure
Every intermediate value consumes registers.
Too many registers reduce occupancy.
Compilation Complexity
A fused kernel may contain dozens of operations.
Generating optimal GPU code becomes difficult.
Hardware Differences
A kernel optimized for:
- NVIDIA H100
- NVIDIA A100
- AMD MI300
may require different strategies.
Debugging
Instead of debugging:
Add
GELU
Multiply
you debug:
FusedAddGeluMultiplyLayerNormKernel_v7
which is considerably less pleasant.
This is one reason projects such as:
- Triton
- NVIDIA CUDA
- TorchInductor
have become increasingly important.
They help automate kernel generation and fusion.
Conclusion
Fused kernels are one of those optimizations that seem almost boring at first glance.
No new model architecture.
No breakthrough algorithm.
No clever prompting technique.
Yet they are responsible for a significant portion of the performance gains that make modern LLM systems practical.
The key insight is simple:
In large-scale AI systems, moving data is often more expensive than computing on it.
Kernel fusion reduces unnecessary memory traffic, keeps data closer to the GPU's compute units, and allows the hardware to spend more time doing useful work.
The next time you hear that a new LLM stack is dramatically faster, don't just ask about quantization, caching, or model architecture.
Ask:
How much of that speedup came from fused kernels?
Question for readers: Have you ever profiled an ML workload and discovered that memory movement—not computation—was the real bottleneck?
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