Radeon RX 7900 XTX vs GeForce RTX 4080 SUPER for Local AI

Quick answer: At the same $999 launch price, the RX 7900 XTX offers 24GB VRAM and 960 GB/s bandwidth while the RTX 4080 SUPER offers 16GB and CUDA \u2014 capacity favors AMD, image speed favors NVIDIA.

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Which GPU is better for local AI workloads?

The 7900 XTX carries 6144 stream processors, 24GB of GDDR6, and 960 GB/s bandwidth for LLM decode. The 4080 SUPER answers with 10240 CUDA cores, 16GB of GDDR6X at 736 GB/s, and NVIDIA\u2019s tensor cores, which generate Stable Diffusion and Flux images faster per unit of bandwidth.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the Radeon RX 7900 XTX.

SpecificationRadeon RX 7900 XTXGeForce RTX 4080 SUPERDifference
VRAM 24 16 +50%
Memory bandwidth 960 736 +30%
Memory type GDDR6 GDDR6X
Memory bus 384 256
TDP 355 320 +11%
CUDA cores 10240
Stream processors 6144
Architecture RDNA 3 Ada Lovelace
Launch MSRP $999 $999
Street price $999 $999

Specs from our sourced product database. See how we source data.

What about price?

Launch MSRP: $999 for both. The 40-series is retiring, so check current prices.

Which one should you buy for LLMs and image generation?

AMD · 2022

Radeon RX 7900 XTX

Buy the 7900 XTX if your priority is large local LLMs \u2014 24GB at this price point is unmatched on paper.

Full specs & benchmarks →
NVIDIA · 2024

GeForce RTX 4080 SUPER

Buy the 4080 SUPER if you prioritize image-generation speed, CUDA-exclusive tools, or training workflows.

Full specs & benchmarks →

Is it faster for LLM inference?

The 7900 XTX\u2019s 24GB loads 30B-class models at Q4 \u2014 models the 4080 SUPER cannot hold \u2014 and its 960 GB/s versus 736 GB/s bandwidth decodes tokens faster. CUDA-exclusive tooling still favors the NVIDIA card.

How does it handle image generation?

The 4080 SUPER generates Stable Diffusion and Flux images faster despite lower bandwidth, because image generation leans on tensor-core FP16 and FP8 compute where Ada holds a large per-core advantage.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: Radeon RX 7900 XTX has 24 GB, GeForce RTX 4080 SUPER has 16 GB.

Fit only on the Radeon RX 7900 XTX (24 GB)

DeepSeek R1-Distill 32B ✓ Qwen 3 32B ✓

Fit on both cards

Gemma 3 27B Wan 2.2 (A14B & TI2V-5B) Mistral Small 3.2 24B FLUX.1 dev Llama 3.1 8B Stable Diffusion 3.5 Large Stable Diffusion XL 1.0 Whisper large-v3 Coqui XTTS-v2

Q4_K_M-equivalent sizes; context and quantization choices shift real limits. Check each model page for full quantization tables.

Frequently asked questions

Which runs bigger LLMs, 7900 XTX or 4080 SUPER?

The 7900 XTX. Its 24GB VRAM holds 30B-class models at Q4, while the 4080 SUPER\u2019s 16GB tops out near 14B at Q8.

Which is faster at Stable Diffusion?

The 4080 SUPER. Image generation is compute-bound on tensor cores, where NVIDIA\u2019s Ada architecture outperforms RDNA 3 per unit of memory bandwidth.

Does the 7900 XTX work with Ollama and ComfyUI?

Yes. ROCm supports RDNA 3 for llama.cpp, Ollama, and ComfyUI on Linux and Windows, though some niche CUDA-only tools remain NVIDIA-exclusive.

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