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Decision Summary
💾 Most VRAM: GeForce RTX 4090
⚡ Best Bandwidth: GeForce RTX 4090
💰 Lowest Price: GeForce RTX 5080
🎯 Best Value: GeForce RTX 5080
🔌 Lowest Power: GeForce RTX 5080
GeForce RTX 4090
Excellent for image generation
Handles Flux, SDXL, and large LoRA stacks with room to spare.
Memory fitFits 70B (Q4 with offloading), 34B (Q8), SDXL, Flux
SoftwareEasy — CUDA ecosystem, everything works out of the box
Build req.750W+ PSU, 3 slots, good airflow
Value$66.63/GB
✓ Buy if• Anyone running 34B+ models or doing serious image generation
• Users wanting maximum software compatibility (CUDA, PyTorch, vLLM, ComfyUI)
• Users wanting maximum software compatibility (CUDA, PyTorch, vLLM, ComfyUI)
✕ Avoid if• Users with PSU or thermal constraints (needs 450W TDP)
↔ Alternatives
Cheaper: GeForce RTX 5080
GeForce RTX 5080
Great for image generation
Runs SDXL and Flux efficiently. Can batch generate.
Memory fitFits 14B (Q8), 8B (unlimited context), SDXL, Flux (optimized)
SoftwareEasy — CUDA ecosystem, everything works out of the box
Build req.650W+ PSU, 2-3 slots
Value$62.44/GB
✓ Buy if• Mainstream AI users wanting 8B-14B models without compromises
• Users wanting maximum software compatibility (CUDA, PyTorch, vLLM, ComfyUI)
• Users wanting maximum software compatibility (CUDA, PyTorch, vLLM, ComfyUI)
↔ Alternatives
More capable: GeForce RTX 4090
Full Specifications
⚡
AI Performance
1 specs
| Specification | GeForce RTX 4090 | GeForce RTX 5080 |
|---|---|---|
| Cuda Cores | 16384 ✓ | 10752 |
💾
Memory & Model Capacity
4 specs
| Specification | GeForce RTX 4090 | GeForce RTX 5080 |
|---|---|---|
| Vram Gb | 24 ✓ | 16 |
| Vram Type | GDDR6X | GDDR7 |
| Memory Bus | 384 ✓ | 256 |
| Memory Bandwidth Gbps | 1008 ✓ | 960 |
🔌
Power, Cooling & Physical
2 specs
| Specification | GeForce RTX 4090 | GeForce RTX 5080 |
|---|---|---|
| Tdp Watts | 450 ✓ | 360 |
| Pcie Interface | PCIe 4.0 x16 | PCIe 5.0 x16 |
📋
General Specifications
1 specs
| Specification | GeForce RTX 4090 | GeForce RTX 5080 |
|---|---|---|
| Architecture | Ada Lovelace | Blackwell |
AI Inference Benchmarks
Tokens per second (higher is better). Benchmark configurations may vary — see methodology.
Sources: www.tomshardware.com, www.myaihardware.com, localaimaster.com, www.hardware-corner.net
| Model | GeForce RTX 4090 | GeForce RTX 5080 |
|---|---|---|
| FLUX.1-dev | — | — |
| Llama-2-13B | — | — |
| Llama-3-70B EST | 18 t/s | — |
| Llama-3-8B | — | — |
| Llama-3.1-8B | 125 t/s | 132 t/s |
| Mistral-7B | — | — |
| Phi-3.5-mini | — | — |
| Qwen3-8B | 104.3 t/s | 94.1 t/s |
| SDXL Turbo | — | — |
| Stable Diffusion 1.5 | — | — |
| Stable Diffusion 1.5 INT8 | — | — |
| Stable Diffusion XL | — | — |
📋 Methodology & Data Quality
Last reviewed: Aug 31, 2026
Scoring version: 1.0.0
Data sources: Manufacturer specifications, community benchmarks (llama.cpp, Ollama, vLLM), MLPerf, Tom's Hardware, Reddit r/LocalLLaMA.
Estimates: Clearly labeled as Estimated or Calculated where used. No fabricated benchmarks.
Limitations: Benchmark configurations vary by source. Normalization is documented in our methodology page.