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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
Great for mainstream LLMs
Can run 70B models in Q4 with offloading, or 8B-34B models comfortably.
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
Good for medium LLMs
Handles 8B-14B models well. 70B requires aggressive quantization or offloading.
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 30, 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.