RTX 3090 vs RX 7900 XTX for local AI — is CUDA worth the premium?

RTX 3090 vs RX 7900 XTX: 24GB CUDA vs 24GB ROCm

Editorial comparison updated 2026-07-26 · live eBay medians refresh daily · Methodology

Verdict: Both are 24GB cards that run the same size models. The RX 7900 XTX is typically the cheapest 24GB you can buy used, and it handles Ollama and llama.cpp chat inference well through ROCm or Vulkan. The 3090 costs more but buys the CUDA ecosystem: faster image generation, mature fine-tuning tools, and day-one support in every new research repo. If your use is 90% chat inference, take the XTX savings; if you will train, generate images, or chase new releases, the 3090 premium pays for itself.

	RTX 3090	RX 7900 XTX
Fair used value	$1,200 ($1,025–$1,425)	$900 ($775–$1,050)
eBay median (filtered)	$1,799 50% over fair	$1,000 11% over fair
Memory	24GB VRAM	24GB VRAM
$ / GB (at fair value)	$50	$38
Power	350W TDP	355W TDP
Launch MSRP	$1,499	$999

Fair values are editorial estimates; eBay medians are filtered Buy-It-Now samples, not checkout prices. See how we compute these.

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What each can run locally

Model class (weights @ Q4)	RTX 3090 · 24GB	RX 7900 XTX · 24GB
7–8B @ Q4 (Llama 3.1 8B, Qwen3 8B)	✓ fits	✓ fits
14B @ Q4 (DeepSeek-R1 14B distill)	✓ fits	✓ fits
30B MoE @ Q4 (Qwen3-coder 30B)	✓ fits	✓ fits
32B dense @ Q4 (Qwen3 32B, R1 32B)	✓ fits	✓ fits
70B dense @ Q4 (Llama 3.3 70B)	— too big	— too big
100B+ MoE (gpt-oss-120b class)	— too big	— too big

Approximate weights-only footprints — long context and KV cache need additional memory headroom.

Where the XTX is genuinely fine

llama.cpp and Ollama have mature ROCm and Vulkan backends, and LM Studio supports AMD out of the box. With about 960 GB/s of memory bandwidth — effectively 3090-class — chat generation speed is competitive, not a compromise.

It is also a 2022 card with no mining-era history, so the used pool is generally in better shape than 3090s of the same price.

Where CUDA still wins

Image generation is the big one: Stable Diffusion and Flux tooling is CUDA-first and markedly faster on NVIDIA. Fine-tuning stacks (bitsandbytes, PEFT, Unsloth) and serving engines like vLLM and TensorRT-LLM either require CUDA or land on it months earlier.

New quantization formats and research code almost always ship CUDA support on day one. On AMD you wait, patch, or skip — that is the real cost of the discount.

The price math

The table above shows live filtered medians for both cards. The gap between them is your CUDA ecosystem tax — decide whether the workloads in the previous section are worth it to you. On pure dollars-per-GB of VRAM, the XTX is regularly the best figure on our entire board.

Shopping notes

On reference-design XTX cards, ask whether the unit was affected by the early vapor-chamber issue (hot-spot throttling); partner boards are unaffected. Coil whine is common but harmless.

For 3090s, apply the usual mining-era checks: thermal pad service history, fan noise, and seller returns on a card this old.

FAQ

Can you run Ollama on an RX 7900 XTX?

Yes. Ollama supports the 7900 XTX through ROCm on Linux and Windows, with Vulkan as a fallback, and 24GB runs the same 30B-class Q4 models a 3090 does at similar chat speeds.

Is the 7900 XTX good for Stable Diffusion?

It works, but it is meaningfully slower than comparable NVIDIA cards and more fiddly to set up. If image generation is a primary use, that alone justifies the 3090.

Which is better for fine-tuning?

The 3090, clearly. The mainstream fine-tuning ecosystem is CUDA-first; ROCm support exists but lags in features, stability, and community help.

Also consider

Model	Memory	Fair value	eBay median
RX 7900 XT	20GB	$650	$700
RTX 4090	24GB	$2,325	$3,199