🎯 The Problem We Solve
Large language models are expensive to run:
- ❌ Attention computation is quadratic in memory O(N²)
- ❌ LayerNorm + GELU use separate kernels (wasted overhead)
- ❌ Models are too large to deploy (FP32 = 4GB per 1B params)
- ❌ Matrix operations waste memory bandwidth
Our Solution: 4 custom CUDA kernels optimized for speed and memory.
What's the Problem?
Standard attention computes a sequence_length × sequence_length matrix in memory.
- For 4K token context: 16M × 4 bytes = 64MB just for attention scores!
- Multiple passes through global memory = slow
How We Fixed It
Flash Attention computes attention in blocks to maximize cache reuse:
- Load Q, K, V tiles into fast shared memory
- Compute attention block-wise
- Use online softmax to avoid storing intermediate results
- Result: 90% less memory, 9.4x faster
128 2048
32 128
📊 Summary of Optimizations
| Operation | Problem | Solution | Speedup |
|---|---|---|---|
| Flash Attention | O(N²) memory | Block-wise computation | 9.4x |
| LayerNorm+GELU | 2 kernels, 2 reads | Single fused kernel | 1.8x |
| Quantization | 4GB models | INT8 compression | 75% smaller |
| GEMM | Poor bandwidth | Shared memory tiling | 2-5x |
🔗 Resources
Built with ❤️ for GPU optimization. Running on Nvidia RTX Pro 6000 with real-time benchmarks.