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Decision Summary
💾 Most VRAM: GeForce RTX 5090
⚡ Best Bandwidth: GeForce RTX 5090
💰 Lowest Price: Radeon RX 7900 XTX
🎯 Best Value: Radeon RX 7900 XTX
🔌 Lowest Power: Radeon RX 7900 XTX
Radeon RX 7900 XTX
Versatile AI performer
Great all-rounder for LLMs, image generation, and most local AI tasks.
Memory fitFits 70B (Q4 with offloading), 34B (Q8), SDXL, Flux
SoftwareModerate — ROCm support improving; some frameworks may need workarounds
Build req.650W+ PSU, 2-3 slots
Value$41.63/GB
✓ Buy if• Anyone running 34B+ models or doing serious image generation
✕ Avoid if• Users who need CUDA-only frameworks without workarounds
↔ Alternatives
More capable: GeForce RTX 5090
GeForce RTX 5090
Top-tier AI performer
Handles any local AI workload including large models and video generation.
Memory fitFits 70B (Q4 with offloading), 34B (Q8), SDXL, Flux
SoftwareEasy — CUDA ecosystem, everything works out of the box
Build req.850W+ PSU, 3+ slots, high airflow case
Value$62.47/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 575W TDP)
↔ Alternatives
Cheaper: Radeon RX 7900 XTX
Full Specifications
⚡
AI Performance
2 specs
| Specification | Radeon RX 7900 XTX | GeForce RTX 5090 |
|---|---|---|
| Cuda Cores | — | 21760 |
| Stream Processors | 6144 | — |
💾
Memory & Model Capacity
4 specs
| Specification | Radeon RX 7900 XTX | GeForce RTX 5090 |
|---|---|---|
| Vram Gb | 24 | 32 ✓ |
| Vram Type | GDDR6 | GDDR7 |
| Memory Bus | 384 | 512 ✓ |
| Memory Bandwidth Gbps | 960 | 1792 ✓ |
🔌
Power, Cooling & Physical
2 specs
| Specification | Radeon RX 7900 XTX | GeForce RTX 5090 |
|---|---|---|
| Tdp Watts | 355 | 575 ✓ |
| Pcie Interface | PCIe 4.0 x16 | PCIe 5.0 x16 |
📋
General Specifications
1 specs
| Specification | Radeon RX 7900 XTX | GeForce RTX 5090 |
|---|---|---|
| Architecture | RDNA 3 | Blackwell |
AI Inference Benchmarks
Tokens per second (higher is better). Benchmark configurations may vary — see methodology.
Sources: www.myaihardware.com, www.tomshardware.com, www.hardware-corner.net
| Model | Radeon RX 7900 XTX | GeForce RTX 5090 |
|---|---|---|
| Llama-3.1-8B | 76 t/s | 215 t/s |
| SDXL Turbo | — | — |
| FLUX.1-dev | — | — |
| Llama-2-13B | — | — |
| Llama-3-70B EST | — | 42 t/s |
| Llama-3-8B | — | — |
| Mistral-7B | — | — |
| Phi-3.5-mini | — | — |
| Qwen3-8B | — | 145.3 t/s |
| 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.