Local AI suitability
The RX 7800 XT offers 16 GB of VRAM with solid bandwidth at a mid-range price, making it one of the better AMD value cards for local AI. It runs 14B models comfortably and 32B models at tight quantization.
Good on Linux with ROCm or on Windows via Vulkan. Like all RDNA 3 cards, the experience depends on runtime support — llama.cpp and Ollama are the most reliable paths.
Recommended model sizes
- 7B (Q6/Q8)
- 14B (Q4/Q5)
- 32B (Q3 with offload)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
Limitations
- ROCm coverage for this SKU is narrower than for the 7900 series on some distributions
- Long contexts on 14B models can exhaust 16 GB
How it compares
Versus the Arc A770 16 GB: the 7800 XT is faster and better supported. Versus the RTX 4070 (12 GB): more VRAM for similar money, with a weaker software ecosystem.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Can the 7800 XT run a 32B model?
At Q3 with CPU offload of some layers, yes, at reduced speed. Entirely on-card, 32B needs more than 16 GB at Q4, so expect compromises.
What runtime works best on RDNA 3?
llama.cpp (Vulkan on Windows, ROCm/HIP on Linux) and Ollama are the most dependable choices for GGUF models.
Is it good for image generation too?
Yes — 16 GB handles Stable Diffusion-class models well through ROCm or ONNX paths, though setup is less turnkey than on NVIDIA.
7800 XT vs RTX 4070 for local AI?
The 7800 XT gives 16 GB versus 12 GB; the 4070 gives CUDA everywhere. If your models fit in 12 GB at the quants you want, the 4070 is simpler; if you need the extra 4 GB, the 7800 XT earns it back.