There is no universal “best” AI chip
Select an accelerator by workload, latency, throughput, flexibility, energy and cost.
Selecting an accelerator
| Type | Best suited to | Important trade-off |
|---|---|---|
| CPU | Control and mixed general-purpose work | Lower parallel throughput |
| GPU | Training, inference and simulation | Power use and data movement |
| NPU / TPU | Neural-network operations | More specialised workloads |
| FPGA | Configurable low-latency pipelines | Programming complexity |
| ASIC | Very high efficiency for a fixed function | Limited flexibility |
| DPU / SmartNIC | Network, storage and security offload | Additional infrastructure complexity |
Selection questions
- What operations dominate the workload?
- Is the priority latency, throughput or energy efficiency?
- How much memory and memory bandwidth are needed?
- Must the hardware support future workload changes?
- Can the software ecosystem use the accelerator efficiently?