GPU comparison
RTX 4080 Super vs RTX 4090 for Stable Diffusion
Draft comparison for later controlled image-generation benchmark research.
Planning summary only. Verify exact GPU variants, runtime support, and workload behavior before making purchase decisions.
RTX 4080 Super
- VRAM
- 16 GB
- Memory type
- GDDR6X
- Bandwidth
- 736.3 GB/s
- Board power / TGP
- 320 W
RTX 4090
- VRAM
- 24 GB
- Memory type
- GDDR6X
- Bandwidth
- 1008 GB/s
- Board power / TGP
- 450 W
FAST ANSWER
Start with the decision that actually changes your workflow
Use this comparison when image-generation planning has moved beyond starter GPUs and the user needs to decide whether 16 GB is enough or 24 GB headroom should be validated.
Are your image settings memory-heavy?
Higher resolution, batch size, and additional processing steps can push a workflow beyond a simple base-model estimate.
No image-speed claim is included
The comparison does not publish seconds-per-image or images-per-minute claims because benchmark evidence is not attached.
Why this comparison matters
The comparison focuses on a 16 GB versus 24 GB planning jump for Stable Diffusion and related image-generation workflows.
For image workflows, capacity pressure can come from resolution, batch size, extensions, ControlNet-like additions, or runtime overhead.
This pair helps users decide whether to validate a bigger local GPU path or test in cloud before buying.
Quick planning summary
RTX 4080 Super: 16 GB | RTX 4090: 24 GB
RTX 4080 Super: 736.3 GB/s | RTX 4090: 1008 GB/s
RTX 4080 Super: Needs verification | RTX 4090: 450 W
Image workflow planning
RTX 4080 Super: medium | RTX 4090: medium
Benchmark evidence, exact board-partner variant, runtime compatibility, and workload fit.
Comparison table
| Field | RTX 4080 Super | RTX 4090 |
|---|---|---|
| Memory planning | ||
| VRAM | 16 GB | 24 GB |
| Memory type | GDDR6X | GDDR6X |
| Memory bus | 256-bit | 384-bit |
| Memory bandwidth | 736.3 GB/s | 1008 GB/s |
| Compute / architecture | ||
| Vendor | NVIDIA | NVIDIA |
| Architecture | Ada Lovelace | Ada Lovelace |
| Core / execution units | 10240 CUDA cores | 16384 CUDA cores |
| Power planning | ||
| Board power / TGP | 320 W | 450 W |
| Power connector | 1 x 16-pin | 1 x PCIe Gen5 (or 3 x 8-pin adapter) |
| Verification | ||
| Status | Source-backed GPU specs available | Source-backed GPU specs available |
| Data confidence | medium | medium |
| Last verified | 2026-05-29 | 2026-05-29 |
Source-backed planning signals
RTX 4090 has more source-backed VRAM headroom than RTX 4080 Super. RTX 4090 has higher listed memory bandwidth than RTX 4080 Super.
Use the RTX 4080 Super vs RTX 4090 signals as prompts for the validation sections below; this component does not add benchmark, price, availability, or purchase claims.
Use this page when these questions match your workflow
Higher resolution, batch size, and additional processing steps can push a workflow beyond a simple base-model estimate.
If the planned workflow is expensive or uncertain, a short cloud test may be more useful than guessing from capacity alone.
A 24 GB path should include PSU, connector, cooling, and case checks before treating it as practical.
Before you trust the comparison
The comparison does not publish seconds-per-image or images-per-minute claims because benchmark evidence is not attached.
Resolution, sampler settings, extensions, precision, and runtime can change memory needs.
Do not assume the 24 GB card is necessary until the intended workflow actually pushes beyond a 16 GB planning path.
What the source-backed data shows
- The linked profiles expose a 16 GB versus 24 GB VRAM planning split.
- The comparison can show source-backed memory and power fields, but not workflow speed.
- Comparison-level image-generation benchmark evidence remains missing.
What still needs validation
- Which image model, runtime, precision, resolution, and batch size will be used?
- Will the workflow include memory-heavy extensions or multi-stage processing?
- Would cloud testing confirm whether 16 GB is enough before local hardware commitment?
PLANNING NEXT STEP
Check model memory before choosing between these GPUs
Run your model assumptions through the VRAM Calculator, then return to GPU profiles for source notes and board-partner verification.
Compare nearby GPU decisions next
Compare when the image workflow may fit in a lower 12 GB versus 16 GB planning tier.
Compare when 24 GB planning also applies to local LLM experiments.
Compare when 24 GB image or AI planning involves cross-vendor runtime questions.
FAQ
Why compare RTX 4080 Super and RTX 4090 for Stable Diffusion?
This pair helps separate a 16 GB local image workflow path from a 24 GB headroom path before making any speed or purchase assumptions.
Does this page prove the RTX 4090 is faster for image generation?
No. The page does not include controlled image-generation benchmarks, so it avoids speed claims and focuses on capacity planning.
When should I test cloud GPU before choosing between these cards?
Cloud testing is useful when resolution, batch size, extensions, or runtime settings may push beyond 16 GB but the user is not sure yet.
Related GPU profiles
Sources and data confidence
RTX 4080 Super
Confidence: medium
Source types: official, database
RTX 4090
Confidence: medium
Source types: official, paper
No benchmark source is attached to this comparison, so benchmark claims are not included.