GPU vs FPGA Decision Framework

A practical guide based on real implementation experience

⚡ Quick Decision Table

Your Requirement Recommended Choice Why
Sub-5ms latency needed GPU 360× faster in this comparison
Power budget <5W FPGA 48× lower power consumption
Batch processing acceptable GPU Throughput scales well with batching
Deterministic latency required FPGA Zero jitter, consistent timing
Model >10M parameters GPU FPGA memory constraints
Edge deployment (1000+ units) FPGA Lower cost at scale
Custom sensor preprocessing FPGA Integrated pipeline on single chip
Need training + inference GPU FPGA inference-only
📊 Can you embed model weights in FPGA BRAM?
(Model <2MB, infrequent updates)
YES → Continue to next question
NO → Choose GPU
↓
⚡ Do you have steady power supply?
(AC power or large battery)
YES → Choose GPU
NO → Continue to next question
↓
🎯 Need deterministic latency <10ms?
(Real-time control, safety-critical)
YES → Choose FPGA
NO → Consider both based on other factors

🚀 Choose GPU If:

  • You have steady power supply (AC or large battery)
  • Batch processing is acceptable
  • You want the easiest development path
  • Sub-5ms latency matters
  • You need training + inference on same hardware
  • Model has >10M parameters
  • You can tolerate latency variability
  • Time-to-market is critical
  • Team lacks FPGA expertise

⚡ Choose FPGA If:

  • Power budget is <5W (battery-powered edge)
  • You can embed model weights in BRAM
  • You need deterministic latency (zero jitter)
  • You're integrating custom sensor preprocessing
  • Deploying thousands of units (cost at scale)
  • Model weights fit in <2MB
  • Infrequent model updates acceptable
  • Team has FPGA design expertise
  • Development time is flexible
🚀 GPU Winner Scenarios
⚡ FPGA Winner Scenarios

⚠️ Don't Choose FPGA If: