Local AI suitability
AMD's flagship RDNA 3 card offers 24 GB of VRAM and near-4090 bandwidth at a lower price. For local AI it is the strongest AMD option, running 32B models at 4-bit entirely in VRAM and competing closely on raw speed where ROCm is supported.
Very good, with a caveat: the software ecosystem. ROCm support on Linux covers llama.cpp, Ollama and vLLM for most popular models, and Vulkan works everywhere including Windows, but tooling still trails CUDA in polish and edge-case coverage.
Recommended model sizes
- 14B (FP16)
- 32B (Q4/Q5)
- 70B (Q2/Q3 with offload)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
Limitations
- ROCm on Windows is limited; Linux gives the best experience
- Some runtimes and quantization paths are CUDA-first and lag on AMD
- Flash-attention-style optimizations may be unavailable depending on runtime version
How it compares
Versus the RTX 4090: similar VRAM and bandwidth for less money, in exchange for a less mature software stack. If you are comfortable on Linux with llama.cpp/Ollama, it is excellent value for 24 GB.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Can the RX 7900 XTX run 70B models?
Not fully on one card at good quality — a Q2 70B barely fits 24 GB. With ROCm offload to system RAM or a second card, Q3/Q4 70B becomes practical at reduced speed.
Does Ollama work on AMD GPUs?
Yes. Ollama supports ROCm on Linux and Vulkan-based inference on Windows for RDNA cards. Performance is competitive with NVIDIA for GGUF models in supported configurations.
Is Windows usable for local AI on AMD?
Yes, mainly through Vulkan (llama.cpp) or DirectML. ROCm's Windows support is improving but still narrower than on Linux, so serious AMD users often run Linux.
7900 XTX vs used RTX 3090?
Both give 24 GB. The 7900 XTX is faster and newer but CUDA-first tooling favors the 3090. Choose based on whether your preferred runtime supports ROCm well.