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

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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.

Specification	Radeon RX 7900 XTX	GeForce RTX 4080 SUPER	Difference
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)

K2 Horizon 36B A4B (MoVA) ✓ Ornith 1.5 35B A3B ✓ Qwen3.6-35B-A3B ✓ Nex N2.5 Mini ✓ K2 Horizon 32B ✓ Edge0 35B A3B (preview) ✓ Qwen-AgentWorld-35B-A3B ✓ Agnes 3.0 Flash ✓ DeepSeek R1-Distill 32B ✓ Qwen 3 32B ✓ Qwen2 5 Coder 32B ✓ Olmo 3 32B Think ✓ Gemma 4 31B IT ✓ Xing4.0-29B-A4B ✓ Qwen3 30B A3B ✓ Qwen3-Coder-30B-A3B-Instruct ✓ Granite 4.2 30B ✓

Fit on both cards

Qwen3.8-27B Qwen3.5-27B Qwen3.6-27B Gemma 3 27B Wan 2.2 (A14B & TI2V-5B) OUI 1 Gemma 4 26B A4B IT Mistral Small 3.2 24B Devstral Small 2 24B Instruct Magistral-Small-2509 Gpt Oss 20B Maple Preview Ling Mini 2.0 Deepseek R1 Distill Qwen 14B Qwen3 14B Confucius4-T3PO WebWorld-14B ContextPilot 14B Phi 4 Phi 3 Medium Ministral 3 14B Instruct Ministral 3 14B Reasoning Gemma 3 12B Mellum 2.1 Thinking FLUX.1 dev Gemma 4 12B IT Ornith 1.5 9B OxCoder 9B Gemma 2 9B K2 Horizon 7B NeoHorse 1 9B Ministral 3 8B Reasoning Ministral 3 8B Instruct Granite 4.2 8B LFM2.5 8B A1B Qwen 3 8B ALoDLM 8B Deepseek R1 Distill Llama 8B Llama 3.1 8B Stable Diffusion 3.5 Large Gemma 4 E4B IT Edge0 8B A1B (preview) Ling-3.0-tiny Qwen2 5 7B ZGCM-1 7B Mistral 7B V03 Gemma 4 E2B IT K2 Horizon 3.7B Gemma 3 4B Ministral 3 3B Reasoning NeoHorse 1 4B Nanbeige4.2 3B Spark X2.5 4B Qwen3 4B Ministral 3 3B Instruct Granite 4.2 3B Stable Diffusion XL 1.0 Llama 3 2 3B Granite Swash 3B A600M LFM2.5 2.6B MiniCPM5 2B Qwen3 1 7B Spark X2.5 1.7B Whisper large-v3 Llama 3 2 1B K2 Horizon 0.9B Coqui XTTS-v2 Qwen3 0 6B GPT-X3 150M

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.