# AMD RX 7900 XTX (24GB) vs NVIDIA RTX 4090 (24GB) for Local AI

> **Direct Hardware Verdict**: Both 24GB cards run across wide 384-bit memory interfaces pushing nearly 1 TB/s (960 GB/s on RX 7900 XTX versus 1,008 GB/s on RTX 4090). For Linux builders focused on Ollama or llama.cpp 32B chat, a used ~$740 RX 7900 XTX delivers ~86% of the 4090's token rate (36 vs 42 tok/s) at less than half the ~$1,650 used RTX 4090 price. Choose the RTX 4090 only when you require native Ada FP8 tensor acceleration, Unsloth QLoRA training, or turnkey Windows ComfyUI video.

## Contender Specifications
| Metric | AMD Radeon RX 7900 XTX (24GB) | NVIDIA GeForce RTX 4090 (24GB) |
|---|---|---|
| VRAM Capacity | 24 GB GDDR6 | 24 GB GDDR6X |
| Memory Bandwidth | 384-bit (960 GB/s) | 384-bit (1,008 GB/s) |
| Typical Price | ~$740 (Used) / ~$929 (New) | ~$1,650 (Used) / ~$1,799 (Retail) |
| Estimated Throughput | ~36 tok/s (32B Q4) / ~88 tok/s (30B-A3B) | ~42 tok/s (32B Q4) / ~95 tok/s (30B-A3B) |

## Comparative Model Compatibility Matrix
| Model & Quantization | VRAM Demand | AMD Radeon RX 7900 XTX | NVIDIA GeForce RTX 4090 |
|---|---|---|---|
| Qwen 2.5 Coder 32B (Q4_K_M, 8k) | 23.1 GB | Runs Comfortably (~36 tok/s) | Runs Comfortably (~42 tok/s) |
| Google Gemma 4 31B (Q4_K_M, 32k FP8 KV) | 22.7 GB | Tight Margin (+1.3GB) | Tight Margin (+1.3GB, Native FP8) |
| Alibaba Qwen3-Coder 30B-A3B (Q4_K_M) | 20.7 GB | Runs Comfortably (~88 tok/s) | Runs Comfortably (~95 tok/s) |
| Alibaba Wan 2.1 14B 720p (Q4) | 18.2 GB | Linux ROCm / Slower DiT | Runs Comfortably (Turnkey CUDA) |
| FLUX.1 [dev] (FP8) | 24.1 GB | Tight / CPU Offload (No FP8 HW) | Tight / Native FP8 Tiled |


## Silicon Architecture & Thermal Analysis
AMD's Navi 31 chiplet package pairs a 5nm Graphics Compute Die (6,144 stream processors, dual-issue FP16/WMMA pipelines, 355W TDP) with six 6nm Memory Cache Dies housing 96MB of second-generation Infinity Cache over a 384-bit 20 Gbps GDDR6 bus (960 GB/s). NVIDIA's monolithic AD102 packs 16,384 CUDA cores, 512 fourth-generation Tensor Cores with dedicated E4M3 FP8 matrix hardware, and 72MB of L2 cache over 21 Gbps GDDR6X (1,008 GB/s) at 450W TDP. Because single-batch 32B Q4 decoding is capped primarily by memory bus speed, Navi 31 trails AD102 by just 14% on raw token output. Yet the instant workloads turn compute-heavy—32k RAG prompt prefill, LoRA backpropagation, or MMDiT denoising—AD102's massive Tensor Core FLOPS, FlashAttention-2, and Triton CUDA wheels finish jobs 2x–3.5x faster without ROCm dependency workarounds.

## Tier 1 Free Software Optimization ($0)
Maximizing an RX 7900 XTX on Ubuntu 24.04? Install ROCm 6.4+ (`gfx1100` native), export `OLLAMA_FLASH_ATTENTION=1` and `OLLAMA_KV_CACHE_TYPE=q8_0`, or pass `-fa on -ctk q8_0 -ctv q8_0` to `llama-server` so Gemma 4 31B (`22.67 GB`) sustains 32k context cleanly while disabling PCIe ASPM power throttling in sysfs.

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## Verified Hardware & Cloud Pathways
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*Interactive Sizing Calculator available at: https://www.gpugarden.com/compare/rx-7900-xtx-24gb-vs-rtx-4090-24gb/*