AMD Radeon RX 9070 XT (16GB) vs NVIDIA RTX 5070 Ti (16GB) for AI & LLMs
AMD Radeon RX 9070 XT (16GB)
NVIDIA GeForce RTX 5070 Ti (16GB)
Popular AI Models Compatibility Matrix
| Model & Quantization | VRAM Demand | AMD Radeon RX 9070 XT | NVIDIA GeForce RTX 5070 Ti |
|---|---|---|---|
| Qwen 2.5 14B Instruct (Q4_K_M) | 10.9 GB | Runs Comfortably (~41 tok/s) | Runs Comfortably (~58 tok/s) |
| OpenAI gpt-oss 20B MoE (Q4_K_M) | 14.0 GB | Runs Comfortably (+2.0GB) | Runs Comfortably (+2.0GB) |
| Google Gemma 4 12B Multimodal (Q4_K_M) | 9.6 GB | Runs Comfortably (+6.4GB) | Runs Comfortably (+6.4GB) |
| FLUX.1 [dev] NF4 / FP8 | 12.0 GB | Runs Comfortably (ROCm/Zluda) | Runs Comfortably (Native CUDA) |
Silicon Architecture, Bandwidth & Thermal Realities
With the Navi 48 die on the Radeon RX 9070 XT, AMD overhauled its compute units for RDNA 4, doubling Wave Matrix Multiply-Accumulate (WMMA) throughput per CU and introducing native hardware FP8 (E4M3/E5M2) and sparsity instructions supported out-of-the-box in ROCm 6.4+ and Vulkan llama.cpp backends. Across its 256-bit 20 Gbps GDDR6 bus (640 GB/s), the RX 9070 XT generates ~41 tok/s on Qwen 2.5 14B Q4_K_M. Meanwhile, NVIDIA's GB203-based RTX 5070 Ti couples a 256-bit bus with 28 Gbps GDDR7 to hit 896 GB/s—nearly matching the previous-generation flagship RTX 3090 (936 GB/s)—while avoiding ROCm wheel compilation friction on Windows and Linux.
On the Radeon RX 9070 XT, run `llama-server` with the native ROCm HIP backend (`-DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1201`) and pass `-fa on -ctk q8_0 -ctv q8_0` to utilize RDNA 4 WMMA instructions and fit 32k context inside 16GB.
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Frequently Asked Questions
Does the AMD RX 9070 XT work well with Ollama and LM Studio on Windows and Linux?
Yes! Both Ollama and LM Studio ship pre-compiled ROCm 6.4 (`gfx1201`) and Vulkan backends for RDNA 4, so running GGUF models like LLaMA 3.1 8B, Gemma 4 12B, and Qwen 2.5 14B works out of the box.
Why is the RTX 5070 Ti 16GB preferred over the RX 9070 XT for ComfyUI and Wan 2.1 video AI?
Video diffusion models (Wan 2.1, HunyuanVideo) rely heavily on PyTorch CUDA custom kernels, SageAttention, and Blackwell FP8/FP4 Tensor Cores, where the RTX 5070 Ti completes renders 2x–2.5x faster than ROCm.
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