Cost per TOPS Comparison
⚡ AI Processing: Cost-per-TOPS Comparison
NVIDIA Data Center vs. Apple Silicon vs. Edge AI Devices — February 2026
💡 Key Insight
Data center GPUs deliver the lowest cost per TOPS ($4–$8/TOPS), but require massive capital outlay ($25K–$55K per chip). Edge devices like the Jetson Orin Nano Super ($3.72/TOPS) and Orange Pi AI Studio Pro ($6.25/TOPS) approach data-center efficiency at consumer-accessible prices. The Raspberry Pi ecosystem offers the lowest total cost of entry but at a higher per-TOPS premium.
🏢 NVIDIA Data Center GPUs
| Device | TOPS | Price | $/TOPS |
|---|---|---|---|
| B200 (SXM) | 9,000 | $40,000 | $4.44 |
| B300 (SXM) | 14,000 | $55,000 | $3.93 |
| H200 (SXM) | 3,958 | $35,000 | $8.84 |
| H100 (SXM) | 3,958 | $30,000 | $7.58 |
| A100 (SXM) | 624 | $7,500 | $12.02 |
| L40S (PCIe) | 1,466 | $8,000 | $5.46 |
🍎 Apple Silicon & 🤖 NVIDIA Edge
| Device | TOPS | Price | $/TOPS |
|---|---|---|---|
| Mac Mini M4 | 38 | $599 | $15.76 |
| Mac Mini M4 Pro | 38 | $1,399 | $36.82 |
| Jetson Orin Nano Super | 67 | $249 | $3.72 |
| Jetson AGX Orin 64GB | 275 | $1,999 | $7.27 |
🍓 Raspberry Pi + AI HATs
| Device | TOPS | Price | $/TOPS |
|---|---|---|---|
| RPi 5 + AI HAT+ 13T | 13 | $150 | $11.54 |
| RPi 5 + AI HAT+ 26T | 26 | $190 | $7.31 |
| RPi 5 + AI HAT+ 2 40T | 40 | $210 | $5.25 |
🍊 Orange Pi AI Solutions
| Device | TOPS | Price | $/TOPS |
|---|---|---|---|
| OPi 4 Pro (8GB) | 3 | $65 | $21.67 |
| OPi 6 Plus (32GB) | 45 | $270 | $6.00 |
| OPi AI Studio (48GB) | 176 | $955 | $5.43 |
| OPi AI Studio Pro (192GB) | 352 | $2,200 | $6.25 |
📊 Cost per TOPS — Visual Comparison (lower is better)
⚖️ Absolute TOPS — Raw AI Throughput
Caveats: INT8 TOPS with sparsity shown for NVIDIA GPUs per their published specs. Apple M4 Neural Engine rated at 38 TOPS. Raspberry Pi system prices include RPi 5 8GB (~$80) + HAT. Orange Pi 6 Plus 45 TOPS = CPU+GPU+NPU combined. Jetson Orin Nano Super: 67 TOPS sparse. Cost-per-TOPS is a rough proxy — real-world performance depends heavily on memory bandwidth, software ecosystem, model compatibility, and precision requirements. NVIDIA DC GPUs support FP8/FP16 training; edge devices are primarily INT8 inference accelerators. Sources: NVIDIA, Apple, Raspberry Pi Foundation, Hailo, Orange Pi, CNX Software, Tom’s Hardware, industry pricing aggregators. Prices as of early 2026.