Different architectures.
Different strengths.
Both execute instructions, but their structures prioritize different kinds of work. The best systems use them together.
Flexible, general-purpose computation and system coordination
High-throughput processing of many similar operations
Fewer, sophisticated cores optimized for varied instruction streams
Many smaller execution units organized for parallel workloads
Strong serial performance with moderate thread-level parallelism
Massive data parallelism when work can follow similar operations
Operating systems, application logic, compilation, simulation control
Graphics, matrix operations, media, suitable AI and scientific workloads
Optimized to finish individual dependent tasks quickly
Optimized for aggregate throughput across a large workload
Large coherent caches and direct access to system memory
High-bandwidth device or unified memory, depending on architecture
Opening an app, running game logic, processing a spreadsheet
Rendering pixels, training models, parallel image filters
Complementary, not competing.
A CPU is often better suited to complex, latency-sensitive control flow. A GPU excels when a large problem can be expressed as many parallel operations. Hardware, software support, data movement, and workload shape the real result.
Explore CPU anatomy