Enhancing AI Performance with CUDA-Native Fused Kernels and Memory-Aware Scheduling Strategies

Modern AI workloads demand more than just raw computing power. Despite advances in GPU architectures, many AI models still face bottlenecks caused by inefficient memory use, frequent kernel launches and unpredictable latency. This post explores how a CUDA-native approach that combines fused kernels, memory-aware scheduling, and deterministic inference paths can significantly improve AI performance on modern GPUs.

Why Traditional AI Acceleration Falls Short

AI models often rely on vendor libraries like cuBLAS and cuDNN, which provide highly optimized kernels for individual operations. Yet, real-world models suffer from several inefficiencies:

• Excessive global memory traffic: Small operations such as normalization, matrix multiplication and activation functions often read and write intermediate results to global memory multiple times. This increases latency and reduces throughput.

• High kernel launch overhead: Running many small kernels sequentially adds launch latency that accumulates and slows down inference.

• Suboptimal key-value (KV) cache access: Autoregressive models depend heavily on KV caches, but poor access patterns can waste memory bandwidth.

• Non-deterministic latency: Dynamic scheduling and resource contention cause unpredictable inference times, which is problematic for production systems requiring stable performance.

  
  

Addressing these challenges requires a shift from isolated kernel optimization to a holistic design that fuses operators and manages memory intelligently.

Fused Kernels for Reduced Memory Traffic and Launch Overhead

Operator fusion combines multiple neural network operations into a single CUDA kernel. This approach reduces the number of times data moves between global memory and GPU cores, cutting down on memory bandwidth use and kernel launch overhead.

For example, fusing attention mechanisms with RMSNorm and linear layers allows the GPU to process these steps in one pass. This eliminates intermediate writes and reads, speeding up inference. Similarly, activation functions can be fused with preceding layers to avoid separate kernel launches.

The 11/11 Labs research demonstrates fused kernels for:

• Attention subgraphs, including FlashAttention variants

• RMSNorm combined with linear transformations

• Activation functions fused with matrix multiplications

  
  

These fused kernels also support mixed precision formats such as FP8, FP16, and BF16, enabling faster computation with minimal accuracy loss.

Memory-Aware Scheduling to Optimize Bandwidth Use

Memory bandwidth often limits AI performance more than raw FLOPs. The research highlights scheduling strategies that reduce global memory traffic and synchronization overhead by:

• Allocating shared memory and registers carefully to keep data close to compute units

• Minimizing synchronization points between threads to avoid stalls

• Organizing KV cache access patterns to maximize cache hits and reduce memory fetches

  
  

By designing kernels and memory layouts together, the system can adapt to tensor shapes, data types and GPU architectures dynamically. This memory-aware scheduling ensures that kernels run efficiently regardless of batch size or sequence length.

Deterministic Inference Paths for Stable Latency

Production AI systems require predictable latency to meet service-level agreements. Dynamic scheduling and resource contention can cause jitter in inference times, which complicates deployment.

The proposed approach includes deterministic inference paths that:

• Fix kernel launch order and resource allocation

• Avoid dynamic kernel selection during inference

• Use precomputed schedules based on input shapes and hardware capabilities

  
  

This design stabilizes latency and improves reproducibility, making it easier to monitor and optimize AI services in production environments.

Practical Implementation and Benchmarking

The 11/11 Labs team provides a reference implementation that selects optimal kernel variants at runtime. This selection depends on:

• Tensor shapes (batch size, sequence length)

• Data types (FP8, FP16, BF16)

• GPU architecture (compute capability, memory hierarchy)

  
  

They also propose a benchmarking protocol measuring:

• End-to-end latency for realistic batch and sequence sizes

• Tokens processed per second (throughput)

• Memory bandwidth utilization

  
  

This protocol helps developers evaluate fused kernels and scheduling strategies under conditions that mimic real-world workloads.

Real-World Impact and Examples

Consider an autoregressive language model generating text token by token. Traditional implementations launch separate kernels for normalization, attention and activation, each reading and writing intermediate results to global memory. This causes delays and unpredictable latency spikes.

Using fused kernels, the model performs these operations in a single pass, reducing memory traffic and kernel launches. Memory-aware scheduling ensures KV cache accesses are efficient, further speeding up generation. Deterministic inference paths guarantee consistent latency, improving user experience in applications like chatbots or real-time translation.

In tests, fused kernels combined with memory-aware scheduling have shown latency reductions of up to 30% and throughput improvements exceeding 20% compared to baseline implementations.

Key Takeaways for AI Developers

• Focus on whole-subgraph fusion rather than optimizing isolated kernels.

• Design kernels with memory layout and scheduling in mind to reduce bandwidth bottlenecks.

• Implement deterministic inference paths to stabilize latency in production.

• Support mixed precision formats like FP8 and BF16 to balance speed and accuracy.

• Use benchmarking protocols that reflect realistic workloads for meaningful performance evaluation.

  
  

By adopting these strategies, developers can unlock the full potential of modern GPUs for AI inference and training.