AI Software and the GPU Industry

A Symbiotic Evolution

June 2025 · Dinesh

The GPU-AI Foundation

Why GPUs Power AI:

Thousands of CUDA cores vs. CPU’s ~10-64 cores
Massive parallelism for tensor operations
Optimized for matrix multiplies and convolutions
High memory bandwidth (HBM3, GDDR6X)

Software Dependencies:

Frameworks rely on GPU libraries (cuDNN, cuBLAS)
Tensor Cores accelerate mixed-precision compute
SIMT execution model matches AI workloads

Software Drives Hardware Innovation

AI Software Explosion:

Large models: GPT, Gemini, Claude demand massive compute
Frameworks push GPU limits: DeepSpeed, vLLM, Triton
Edge AI opens new markets: Jetson, CoreML

Hardware-Software Co-Evolution

**Feedback Loop:** ``` AI Software Needs → GPU Architecture Changes ↑ ↓ Performance Bottlenecks ← New Hardware Features ``` **Real Examples:** - **Mixed-precision:** FP16, bfloat16, FP8 support - **Communication:** NCCL, NVLink for multi-GPU - **Memory:** SRAM improvements for transformer models - **Specialized units:** Transformer Engines in Hopper

![Co-Evolution Cycle](image.png)

Future Trends & Summary

Emerging Trends:

Open-source GPU stacks (ROCm, Triton)
Multi-backend compilers (IREE, TVM)
Cross-hardware abstractions
Energy-efficient “Green AI”

Key Takeaways:

AI software and GPUs are deeply interdependent
Software innovation drives GPU adoption and design
GPUs enable software breakthroughs through scale
Co-evolution defines the performance frontier

Thank You

Dinesh 📧 dineshkumarb@gmail.com 🌐 https://dkbhaskaran.github.io/ 📞 +1 999 999 9999