DEFINITION
CUDA is NVIDIA’s parallel computing platform for writing programs that run on GPUs. AI developers learn it to understand performance, memory, and custom kernels when frameworks alone leave performance on the table.
Framework first, CUDA when needed
Most training and inference uses PyTorch/TensorRT/vLLM. CUDA skills help when profiling shows kernel or memory bottlenecks, when writing fused ops, or when operating multi-GPU clusters.
Concepts that transfer
Warps, occupancy, memory hierarchy, and host/device transfers explain why batch sizes, sequence lengths, and KV-cache strategies dominate LLM serving cost.
FAQ
Is CUDA only for research?
No. Production inference teams use GPU systems knowledge daily—even when they rarely write raw kernels.
Expert
Deepskilling Cloud & Data Faculty — Programme Lead, Cloud, Data Engineering & System Design. Mentors for AWS/Azure certification paths, data engineering on AWS/GCP, and fundamentals-to-mastery system design.
Research citations
- Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon, W. et al. (2023). SOSP. Serving systems research behind high-throughput LLM inference (vLLM).