cuBLAS is the NVIDIA GPU implementation of BLAS routines for dense linear algebra, especially matrix multiplication - it powers the GEMM-heavy compute core of modern neural network training and inference.

What Is cuBLAS?

• Definition: CUDA library for vector and matrix operations, including highly optimized GEMM kernels.
• Core Relevance: Linear layers and many attention projections map directly to GEMM workloads.
• Precision Modes: Supports FP32, FP16, BF16, and tensor-core accelerated math modes.
• Companion Stack: Often paired with cuBLASLt for advanced epilogue fusion and layout options.

Why cuBLAS Matters

• Throughput Driver: GEMM performance strongly determines overall model step time.
• Hardware Utilization: Tuned kernels maximize occupancy and tensor-core usage on supported devices.
• Numerical Control: Allows precision-performance tradeoffs through configurable math modes.
• Ecosystem Standard: Most frameworks rely on cuBLAS as their default dense math backend.
• Optimization Anchor: Profiling against cuBLAS throughput helps identify true compute bottlenecks.

How It Is Used in Practice

• Shape Planning: Align matrix dimensions to tensor-core-friendly sizes when designing model blocks.
• Backend Selection: Use cuBLASLt interfaces for fused epilogues and layout-specific speed gains.
• Benchmark Discipline: Track achieved FLOPs versus theoretical peaks to prioritize further tuning.

cuBLAS is a central performance dependency for transformer-scale workloads - strong GEMM efficiency is mandatory for competitive GPU training speed.