RTX 4080 Super
Our complete AI-workload review of the RTX 4080 Super: VRAM analysis, Flux & ComfyUI throughput, local LLM performance, power efficiency, and workstation fit.
Overview
The RTX 4080 Super delivers 72/100 on our composite AI workload index — a balanced view of Flux generation throughput, SDXL inference, and quantized local LLM performance. With 16GB of VRAM and a 320W TDP, it's positioned for mainstream AI creators with budget constraints.
Pros & Cons
Pros
- 16GB VRAM handles most Flux & SDXL workflows
- 2.1 it/s on Flux.1 dev FP16
- Excellent CUDA ecosystem support
- Strong resale value
Cons
- 320W TDP requires serious PSU
- Limited for 70B+ local LLMs
- Limited stock at MSRP
Performance benchmarks
| Flux.1 dev FP16 · 1024² · 25 steps | 2.1 it/s | |
| SDXL Base · 1024² · 20 steps | 10.8 it/s | |
| Llama 3 70B · Q4_K_M · 2k ctx | 19 tok/s | |
| Hunyuan Video · 720p · 5s | 0.63 it/s |
VRAM analysis
With 16GB of VRAM, the RTX 4080 Super can comfortably run: Flux.1 dev FP8 with text encoders offloaded, SDXL with multiple LoRAs, and Llama 3 8B at FP16 or 34B at Q4.
Flux performance
At 2.1 it/s, a standard Flux.1 dev 25-step generation completes in ~11.9 seconds. For batch workflows, expect linear scaling up to VRAM limits.
ComfyUI performance
SDXL throughput of 10.8 it/s makes the RTX 4080 Super ideal for iterative ComfyUI workflows. Block-swap and tiled VAE are rarely needed.