NVIDIA GPU Inference Benchmarks for Edge AI 2026 — RTX 5090, L40S & H100 Throughput Compared

Published: July 5, 2026 | Category: Technical | QSCompute

Not all GPUs are created equal for edge AI inference. The RTX 5090 brings 32 GB of GDDR7 and 1.8× the memory bandwidth of its predecessor — but how does that translate to real inference throughput against the datacenter-grade L40S and H100? We ran benchmarks across three workload profiles to give you the numbers that matter: tokens per second for LLM serving, images per second for vision pipelines, and cost-per-inference for budget planning.

GPU Specs at a Glance

GPU	VRAM	Bandwidth	FP16 TFLOPS	TDP	Street Price (Q3 2026)
RTX 5090	32 GB GDDR7	1,792 GB/s	104.8	575 W	$1,999
RTX 6000 Ada	48 GB GDDR6 ECC	960 GB/s	91.1	300 W	$6,800
NVIDIA L40S	48 GB GDDR6 ECC	864 GB/s	91.6	350 W	$8,500
NVIDIA H100	80 GB HBM3	3,350 GB/s	989.4	700 W	$28,000

Benchmark 1: LLM Inference — Llama 3.1 8B (FP16)

Measured with vLLM v0.6.0, batch size 32, input 512 tokens, output 128 tokens:

GPU	Tokens/sec	P99 Latency	Max Batch Size	Cost per 1M Tokens
RTX 5090	3,240	142 ms	64	$0.08
RTX 6000 Ada	2,180	198 ms	64	$0.19
L40S	2,410	185 ms	96	$0.21
H100	8,960	52 ms	256	$0.18

Key takeaway: The RTX 5090 delivers the best cost-per-token by a wide margin for 8B-class models, thanks to its GDDR7 bandwidth and consumer pricing. The L40S pulls ahead at larger batch sizes (96+) and when ECC is mandatory, but for most edge deployments running 1–4 concurrent inference streams, the 5090 is the smarter dollar.

Benchmark 2: Vision — YOLOv8x (640×640, FP16 TensorRT)

GPU	Images/sec (BS=1)	Images/sec (BS=8)	P99 Latency (BS=1)
RTX 5090	1,240	4,860	1.2 ms
RTX 6000 Ada	980	4,120	1.5 ms
L40S	1,050	4,520	1.4 ms
H100	2,340	9,800	0.7 ms

For factory-floor AOI (automated optical inspection) running 4–16 cameras at 30 FPS, the RTX 5090 handles the entire stream at batch size 8 with headroom to spare. The L40S only makes sense if you need ECC memory for regulated environments (medical imaging, aerospace inspection).

Benchmark 3: Diffusion — Stable Diffusion XL (1024×1024, FP16)

GPU	Images/sec (BS=1)	Images/sec (BS=4)	VRAM Used
RTX 5090	8.2	24.6	18.4 GB
RTX 6000 Ada	5.8	18.4	22.1 GB
L40S	6.1	20.2	21.8 GB
H100	19.4	62.8	28.6 GB

SDXL fits comfortably in all four GPUs. The RTX 5090's 24.6 img/sec at batch 4 outperforms the L40S by 22%, making it the go-to for on-premise creative AI and design-tool edge deployments.

Which GPU for Your Edge AI Workload?

Use Case	Recommended GPU	Why
LLM serving (≤13B params), single stream	RTX 5090	Best cost-per-token, 32 GB VRAM handles 13B comfortably
Multi-camera AOI (8–16 streams)	RTX 5090	Enough throughput at BS=8, low latency per image
Medical / regulated inference	RTX 6000 Ada or L40S	ECC memory required for compliance
High-concurrency serving (50+ streams)	L40S or H100	Larger batch capacity, enterprise reliability
LLM serving (70B params)	H100 × 2 or more	80 GB HBM3 handles 70B in single GPU; tensor parallelism across 2+ GPUs for speed
On-premise creative AI (SDXL, video gen)	RTX 5090	Fastest consumer card for diffusion, best $/image

QSCompute Pre-Configured GPU Edge Servers

QS-GPU-5090-Edge

Single RTX 5090 in a compact tower. AMD Ryzen 9 9950X, 64 GB DDR5 ECC, 2 TB NVMe RAID 1. Ubuntu 24.04 with CUDA 12.8, vLLM, TensorRT pre-installed.

$4,999

In Stock — Ships in 5 Days

QS-GPU-L40S-Pro

Single L40S in a 2U rack-mount. Dual Xeon Silver 4510, 128 GB DDR5 ECC, 4 TB NVMe. Dual 25 GbE, IPMI. For high-concurrency production inference.

$14,800

In Stock — Ships in 7 Days

QS-GPU-Dual-5090

Dual RTX 5090 with NVLink bridge in a tower. Threadripper 7970X, 128 GB DDR5 ECC, 4 TB NVMe. For 70B model inference with tensor parallelism across 2 GPUs.

$9,499