Radeon RX 9070 XT vs GeForce RTX 5070 Ti for Local AI

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Which GPU is better for local AI workloads?

Both are 16GB cards aimed at enthusiasts. The 5070 Ti offers 896 GB/s memory bandwidth versus 640 GB/s on the 9070 XT, so token generation is faster on NVIDIA. The 9070 XT counters with a $599 launch price and official ROCm 7.x support on Windows for mainstream AI tools.

Spec comparison: what actually differs

The table below is computed live from our hardware database. Positive deltas favor the Radeon RX 9070 XT.

Specification	Radeon RX 9070 XT	GeForce RTX 5070 Ti	Difference
VRAM	16	16	—
Memory bandwidth	640	896	-29%
Memory type	GDDR6	GDDR7	—
Memory bus	256	256	—
TDP	304	300	+1%
CUDA cores	—	8960	—
Stream processors	4096	—	—
Architecture	RDNA 4	Blackwell	—
Launch MSRP	$599	$749	-20%
Street price	$599	$749	-20%

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

What about price?

Launch MSRP: $599 (9070 XT) versus $749 (5070 Ti).

Which one should you buy for LLMs and image generation?

AMD · 2025

Radeon RX 9070 XT

Buy the 9070 XT if budget matters and your stack is llama.cpp/Ollama/ComfyUI — the $150 saved buys half a 9070.

Full specs & benchmarks →

NVIDIA · 2025

GeForce RTX 5070 Ti

Buy the 5070 Ti if you want maximum speed at 16GB and day-one compatibility with every AI framework.

Full specs & benchmarks →

Is it faster for LLM inference?

For llama.cpp and Ollama both cards run 14B-class models in 16GB; the 5070 Ti decodes faster on paper (896 vs 640 GB/s), while the 9070 XT is a capable value alternative with ROCm support now official for RDNA4.

How does it handle image generation?

ComfyUI and SDXL run on both. AMD's ROCm path for image generation has matured but NVIDIA still has fewer edge cases with custom nodes and newer models.

Which AI models fit on each card?

Computed from our model VRAM database at Q4 quantization: Radeon RX 9070 XT has 16 GB, GeForce RTX 5070 Ti has 16 GB.

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

Is the RX 9070 XT good for local LLMs?

Yes. Its 16GB VRAM fits 14B models at Q8, and ROCm 7.x officially supports the RDNA4-based 9070 XT for llama.cpp and Ollama. NVIDIA still leads in raw bandwidth: 896 GB/s vs 640 GB/s.

Which is faster for Stable Diffusion?

The 5070 Ti, by roughly the bandwidth and tensor-core gap. The 9070 XT is close enough that price-per-image favors it at its $599 launch price.

Do AMD cards work with every AI tool?

Most mainstream tools (llama.cpp, Ollama, ComfyUI, PyTorch via ROCm) now support RDNA4. CUDA-only research code and some niche custom nodes still require NVIDIA.