RTX 5080 vs. 3090: Is the VRAM Trade-Off Worth It for AI Workloads?

RTX 5080 vs. 3090: Is the VRAM Trade-Off Worth It for AI Workloads?

As NVIDIA’s RTX 5080 begins to appear on the market, a growing cohort of AI content creators and generative model enthusiasts are grappling with a critical dilemma: is the 16GB VRAM downgrade from the 24GB RTX 3090 worth the leap to the new Blackwell architecture? A detailed Reddit thread from r/StableDiffusion, posted by user HieeeRin, highlights the tension between raw performance gains and memory capacity in high-stakes AI workflows.

HieeeRin’s rig — powered by an AMD R9 9950X and 64GB DDR5-6000 RAM — currently relies on the RTX 3090 for intensive tasks like SDXL image generation, LoRA training, and video synthesis. The proposed upgrade to the RTX 5080 promises faster tensor operations, improved memory bandwidth, and enhanced efficiency, but at the cost of 8GB of VRAM. The central question: can the 5080 reliably run batch size 3 at 896x1152 resolution with SDXL/Illustrious models without resorting to system RAM swaps, or will the 3090 remain the superior daily driver despite its older architecture?

Current benchmarks for SDXL on 16GB cards are limited, but early tests from AI benchmarking communities suggest that batch size 2 at 896x1152 is generally stable on 16GB cards using optimized inference engines like Forge Neo. Batch size 3, however, pushes the limits — especially with larger models like Z-Image, Turbo, and Anima, which require substantial activation memory. While the 5080’s new memory compression algorithms and improved cache hierarchy may reduce memory pressure, the 3090’s 24GB buffer remains a safety net that eliminates OOM errors outright. According to users on Reddit, the 5080 may achieve higher FPS and faster per-image generation times, but only if the workload fits within its memory ceiling.

For LoRA training — HieeeRin’s second most critical workload — the Blackwell architecture’s architectural advantages are more decisive. The 5080’s enhanced Tensor Core efficiency, higher FP8 throughput, and improved memory bandwidth (estimated at 720 GB/s vs. 936 GB/s on the 3090, though with better utilization) could significantly accelerate training iterations. While the 3090 can handle KohyaSS training without issue, it often runs at 95-100% VRAM utilization, forcing users to reduce batch sizes or use gradient checkpointing. The 5080, despite having less VRAM, may complete epochs faster due to superior compute density — provided training datasets are optimized to fit within 16GB. Users report that with mixed-precision training and 8-bit Adam optimizers, 16GB cards can train LoRAs on 512x512 to 896x896 resolutions with batch sizes of 4-6, which may be sufficient for many creators.

Video generation via WAN2.2 at 720p is less demanding and can be offloaded to the 3090 via Thunderbolt, as HieeeRin plans. Similarly, LLM inference using LM Studio is already offloaded, making the 5080’s role primarily focused on image and training workloads. The real test lies in the 50% of time spent on SDXL generation. If the 5080 can maintain stable batch size 3 without frequent memory thrashing — a scenario supported by early adopters who use xFormers and TensorRT optimizations — the performance gains could justify the VRAM sacrifice.

Ultimately, the RTX 5080 is not a direct replacement for the 3090, but a complementary upgrade. For users who can adapt their workflows — reducing batch sizes slightly, leveraging memory-efficient pipelines, and keeping the 3090 as a backup — the 5080 offers a compelling leap in speed and efficiency. However, those who demand zero compromises on batch size or model complexity may find the 3090’s 24GB VRAM irreplaceable. The verdict: the 5080 is worth it for workflow optimization, not raw capacity.