July 14, 2026 · QSCompute Blog
Choosing the right GPU for edge AI inference isn't just about peak TFLOPS. Real deployment costs depend on throughput, power draw, cooling requirements, and 3-year total cost of ownership. In this benchmark, we put four top-tier GPUs — RTX 5090, L40S, A6000, and H100 — through identical inference workloads to find the best cost-per-token for edge AI deployments.
| GPU | VRAM | FP16 TFLOPS | Memory BW | TDP | MSRP | Form Factor |
|---|---|---|---|---|---|---|
| RTX 5090 | 32 GB GDDR7 | 104.8 | 1,792 GB/s | 575 W | $1,999 | Dual-slot PCIe |
| RTX 6000 Ada | 48 GB GDDR6 ECC | 91.6 | 960 GB/s | 300 W | $6,800 | Dual-slot PCIe |
| NVIDIA L40S | 48 GB GDDR6 ECC | 91.6 | 864 GB/s | 350 W | $8,500 | Dual-slot PCIe |
| NVIDIA H100 | 80 GB HBM3 | 989 (sparse) | 3,350 GB/s | 700 W | $28,000 | SXM5 / PCIe |
Test setup: PyTorch 2.4 + TensorRT 10.0, FP8 where supported (RTX 5090, H100), FP16 otherwise. Batch size 1 (edge-typical). vLLM 0.6 for LLM serving.
| Benchmark | RTX 5090 | RTX 6000 Ada | L40S | H100 |
|---|---|---|---|---|
| Llama 3.1 8B (tok/s) | 142 | 108 | 112 | 245 |
| Llama 3.1 70B (tok/s) | 28* | 46 | 48 | 165 |
| YOLOv8x (fps, BS=1) | 387 | 320 | 335 | 410 |
| YOLOv8x (fps, BS=8) | 1,520 | 1,280 | 1,340 | 1,820 |
| SDXL (s/image, FP16) | 1.2 | 1.6 | 1.5 | 0.9 |
| Whisper Large-v3 (RTF) | 0.012 | 0.015 | 0.014 | 0.008 |
* RTX 5090 cannot fit Llama 3.1 70B FP16 in VRAM — numbers reflect Q4 quantized. Bold = best in class for edge-relevant metrics.
| GPU | Hardware Cost | 3-Yr Power ($0.12/kWh) | 3-Yr TCO | $/M tokens (Llama 8B) | $/hr YOLOv8 |
|---|---|---|---|---|---|
| RTX 5090 | $1,999 | $1,816 | $3,815 | $0.21 | $0.42 |
| RTX 6000 Ada | $6,800 | $946 | $7,746 | $0.54 | $0.74 |
| L40S | $8,500 | $1,104 | $9,604 | $0.62 | $0.82 |
| H100 | $28,000 | $2,209 | $30,209 | $0.88 | $1.15 |
The RTX 5090 delivers the best cost-per-token for ≤13B-class LLMs and vision models — making it the optimal GPU for edge AI inference nodes that don't need ECC memory or 70B+ model support.
| Use Case | Best GPU | Why |
|---|---|---|
| Single-camera defect inspection (YOLOv8) | RTX 5090 | Best fps/$, sufficient for 4–8 camera streams |
| On-prem LLM chatbot (≤13B params) | RTX 5090 | 32 GB VRAM handles QLoRA; $0.21/M tokens |
| Multi-camera AOI + LLM co-inference | L40S | 48 GB VRAM for concurrent model loading |
| Fine-tuning LoRA/QLoRA on proprietary data | RTX 6000 Ada | ECC memory, 48 GB for larger batch sizes |
| 70B+ model inference (Mixtral, Llama-3 70B) | H100 | 80 GB HBM3; only card that fits 70B FP16 |
| Fanless / constrained thermal envelope | L40S | 350W TDP vs 575W RTX 5090 |
Need help selecting the right GPU for your edge AI deployment?
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