# Used RTX 4080 Super vs RTX 5080 (16GB) for Local LLMs

> **Direct Hardware Verdict**: Neither card solves the fundamental 16GB capacity cap for 80-class buyers. Spending $1,099 on a new RTX 5080 increases memory bandwidth by +30.4% over a ~$890 used RTX 4080 Super (960 vs 736 GB/s), pushing Qwen 2.5 14B Q4_K_M from ~61 to ~80 tok/s alongside native Blackwell NVFP4 support. However, spending $890–$1,099 for 16GB leaves zero room for unquantized 32B coders—making the cheaper RTX 5070 Ti ($799) or used 24GB RTX 3090 (~$680) smarter buys.

## Contender Specifications
| Metric | NVIDIA GeForce RTX 4080 Super (16GB) | NVIDIA GeForce RTX 5080 (16GB) |
|---|---|---|
| VRAM Capacity | 16 GB GDDR6X | 16 GB GDDR7 |
| Memory Bandwidth | 256-bit (736 GB/s) | 256-bit (960 GB/s) |
| Typical Price | ~$890 (Used) | ~$1,099 (New) |
| Estimated Throughput | ~61 tok/s (14B Q4) / ~43 tok/s (20B MoE) | ~80 tok/s (14B Q4) / ~56 tok/s (20B MoE) |

## Comparative Model Compatibility Matrix
| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 4080 Super | NVIDIA GeForce RTX 5080 |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q4_K_M, 32k FP8) | 14.06 GB | Tight Fit (+1.94 GB, ~61 tok/s) | Tight Fit (+1.94 GB, ~80 tok/s) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.43 GB | Tight Fit (+1.57 GB, ~43 tok/s) | Tight Fit (+1.57 GB, NVFP4 ~56 tok/s) |
| Meta LLaMA 3.1 8B (Q8_0) | 11.69 GB | Comfort Margin (~60 tok/s) | Comfort Margin (~79 tok/s) |
| FLUX.1 [schnell] 4-bit | 14.46 GB | Tight Fit (+1.54 GB) | Tight Fit (+1.54 GB, Fast FP4) |
| Google Gemma 4 26B-A4B (Q4_K_M) | 17.14 GB | Exceeds VRAM (-1.14 GB Swap) | Exceeds VRAM (-1.14 GB Swap) |


## Silicon Architecture & Thermal Analysis
Comparing full AD103 silicon against full GB203 silicon isolates the architectural shift from top-bin GDDR6X to first-generation GDDR7. The RTX 4080 Super clocks 23 Gbps GDDR6X across a 256-bit bus (736 GB/s) backed by a spacious 64 MB L2 cache and 10,240 CUDA cores at 320W TDP. The RTX 5080 retains an identical 256-bit trace layout but installs 30 Gbps GDDR7 modules (960 GB/s), 10,752 shaders, PCIe 5.0 lanes, and 5th-gen NVFP4 Tensor Cores at 360W. Because dense 14B checkpoints completely overflow even 64 MB of L2 cache during every token step, the RTX 5080's +224 GB/s external memory advantage translates straight into ~30% faster decoding. Still, 16GB traps Gemma 4 26B-A4B (17.14 GB) and Wan 2.1 14B (18.15 GB) in offload.

## Tier 1 Free Software Optimization ($0)
Before selling an RTX 4080 Super for an RTX 5080, run `llama-server -m model.gguf -ngl 99 -fa on -ctk q8_0 -ctv q8_0` on dense 14B models and selectively push cold experts on 30B MoE models to DDR5 via `--n-cpu-moe 12`, preserving crisp responsiveness for $0.

- Companion Upgrade (Crucial Pro 64GB (2x32GB) DDR5-5600 Kit): https://www.amazon.com/dp/B0C79H54TQ?tag=sparecables-20 (rel="noopener sponsored")

## Verified Hardware & Cloud Pathways
- Shop Tested Used on eBay (~$890) ↗: https://www.ebay.com/sch/i.html?_nkw=NVIDIA%20GeForce%20RTX%204080%20Super%2016GB%20GPU&LH_BIN=1&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339245689&toolid=10001&mkevt=1&customid=gpugarden_programmatic_md (rel="noopener sponsored")
- Compare New Prime GPUs on Amazon (NVIDIA GeForce RTX 5080 16GB GDDR7): https://www.amazon.com/dp/B0DS2R6948?tag=sparecables-20 (rel="noopener sponsored")
- Rig Essential — Thermal Grizzly PhaseSheet PTM: https://www.amazon.com/dp/B0DB84CQW6?tag=sparecables-20 (rel="noopener sponsored")
- Rig Essential — upHere GPU Anti-Sag Bracket: https://www.amazon.com/dp/B076GYL25H?tag=sparecables-20 (rel="noopener sponsored")
- NVIDIA GeForce RTX 5080 16GB GDDR7 on Amazon Prime: https://www.amazon.com/dp/B0DS2R6948?tag=sparecables-20 (rel="noopener sponsored")
- Rent on RunPod: https://console.runpod.io/deploy?ref=ynypzu94 (rel="noopener sponsored")
- Rent on Vast.ai: https://cloud.vast.ai/?ref_id=723130 (rel="noopener sponsored")

---
*Interactive Sizing Calculator available at: https://www.gpugarden.com/compare/rtx-4080-super-vs-rtx-5080-16gb/*