| 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) |
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.
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.
Matched Upgrade: Because both 80-class cards share a hard 16GB VRAM ceiling, investing in 64GB DDR5 memory keeps hybrid MoE expert offloading smooth rather than stalling against 32GB pagefile limits.
Crucial Pro 64GB DDR5 Kit on Amazon Prime ↗Zero monthly subscriptions, 100% private inference, and unmetered tokens for your homelab or workstation.
Skip the upfront hardware expense. Spin up secure cloud instances from $0.35/hr on Vast.ai or $0.79/hr on RunPod.
No. After selling an RTX 4080 Super for ~$890 and paying $1,099 plus tax, you absorb a $250+ net loss only to hit the exact same 16GB ceiling. Jump to 24GB–32GB (used RTX 3090, RTX 4090, or RTX 5090) instead.
Between strictly these two SKUs, the brand-new $1,099 RTX 5080 beats an $890 used RTX 4080 Super via +30.4% memory bandwidth, native NVFP4 support, and full warranty coverage—yet stepping down to the $799 RTX 5070 Ti captures 93% of its bandwidth.
Inspect exact KV-cache formulas, test 32k/128k context allocations, or compare cloud rental break-even costs in the interactive matchmaker.
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