# NVIDIA RTX 3090 (24GB) vs AMD RX 7900 XTX (24GB) for Local AI

> **Direct Hardware Verdict**: Both cards match 24GB capacity on 384-bit buses with nearly identical memory bandwidth (936 vs 960 GB/s), delivering ~37–38 tok/s on Gemma 4 31B Q4_K_M. However, the used RTX 3090 costs ~$680 (often $60–$250 less than a 7900 XTX), works seamlessly with PyTorch CUDA, bitsandbytes, FlashAttention-2, and ComfyUI video nodes on Windows or Linux, and offers 112.5 GB/s NVLink expansion for future 48GB builds. Buy the RX 7900 XTX only if you want a new-in-box Linux ROCm card.

## Contender Specifications
| Metric | NVIDIA GeForce RTX 3090 (24GB) | AMD Radeon RX 7900 XTX (24GB) |
|---|---|---|
| VRAM Capacity | 24 GB GDDR6X | 24 GB GDDR6 |
| Memory Bandwidth | 384-bit (936 GB/s) | 384-bit (960 GB/s) |
| Typical Price | ~$680 (Used) | ~$740 (Used) / ~$929 (New) |
| Estimated Throughput | ~37 tok/s (31B Q4) · Native CUDA | ~38 tok/s (31B Q4) · Linux ROCm |

## Comparative Model Compatibility Matrix
| Model & Quantization | VRAM Demand | NVIDIA GeForce RTX 3090 | AMD Radeon RX 7900 XTX |
|---|---|---|---|
| Google Gemma 4 31B (Q4_K_M) | 21.44 GB | Fits Clean (+2.56 GB, CUDA) | Fits Clean (+2.56 GB, ROCm) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.66 GB | Resident (+3.34 GB, FlashAttn) | Resident (+3.34 GB, HIP/Vulkan) |
| Alibaba Wan 2.1 14B 720p (Q4 / FP8) | 18.15 GB | Native SageAttention (+5.85 GB) | Linux ROCm Only (+5.85 GB, Slow) |
| FLUX.1 [dev] NF4 / Q4 | 15.46 GB | Native bitsandbytes (+8.54 GB) | GGUF Q4 via ROCm (+8.54 GB) |
| Meta LLaMA 3.3 70B (Q4_K_M) | 46.10 GB | Needs 2nd 3090 + NVLink | Exceeds VRAM (-22.10 GB OOM) |


## Silicon Architecture & Thermal Analysis
Underneath their matched 384-bit controllers, Ampere GA102 and RDNA 3 Navi 31 solve high-bandwidth inference differently. AMD splits Navi 31 (`gfx1100`) into a 5nm Graphics Compute Die flanked by six 6nm Memory Cache Dies housing 96 MB of 2nd-generation Infinity Cache and 20 Gbps GDDR6 (960 GB/s). In memory-bound GGUF decoding via llama.cpp Vulkan or Linux ROCm HIP, the 7900 XTX slightly edges out the RTX 3090's 936 GB/s GDDR6X. Yet RDNA 3 lacks dedicated Tensor Core hardware equivalent to Ampere's 2:4 sparsity and TF32/BF16 matrix engines, relying on dual-issue Wave32 WMMA instructions. Consequently, PyTorch video diffusion stacks (Wan 2.1, HunyuanVideo, SageAttention) and vLLM quantized AWQ/GPTQ kernels often require custom ROCm containers or Windows ZLUDA workarounds, whereas GA102 runs upstream wheels immediately.

## Tier 1 Free Software Optimization ($0)
Already running an AMD RX 7900 XTX? Boot Ubuntu Linux with official ROCm 6.4+ drivers, set `export HSA_OVERRIDE_GFX_VERSION=11.0.0` only when legacy PyTorch wheels fail to recognize `gfx1100`, and launch `llama-server -m model.gguf -ngl 99 -fa on -ctk q8_0 -ctv q8_0` (or `--device Vulkan0` on Windows) for reliable throughput.

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## Verified Hardware & Cloud Pathways
- Shop Tested Used on eBay (~$680) ↗: https://www.ebay.com/sch/i.html?_nkw=NVIDIA%20GeForce%20RTX%203090%2024GB%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")
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- Shop Tested Used on eBay (~$740) ↗: https://www.ebay.com/sch/i.html?_nkw=AMD%20Radeon%20RX%207900%20XTX%2024GB%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")
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*Interactive Sizing Calculator available at: https://www.gpugarden.com/compare/rtx-3090-vs-rx-7900-xtx-24gb/*