RTX 5060 Ti 16GB vs Radeon RX 9060 XT 16GB for Local LLMs
Both have 16.0 GB, so they run the same models. The RTX 5060 Ti 16GB generates tokens about 64% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| RTX 5060 Ti 16GB | Radeon RX 9060 XT 16GB | |
|---|---|---|
| Memory | 16 GB | 16 GB |
| GPU-usable memory | 16.0 GB | 16.0 GB |
| Bandwidth | 448 GB/s | 320 GB/s |
| Architecture | Blackwell | RDNA 4 |
| Released | 2025-04 | 2025-06 |
| Llama 3.1 8B Q4_K_M | ~53 tok/s | ~32 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | RTX 5060 Ti 16GB | Radeon RX 9060 XT 16GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~53 tok/s | Fits ~32 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~30 tok/s | Fits ~18 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~92 tok/s | Fits ~61 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | 80% on GPU ~44 tok/s | 80% on GPU ~37 tok/s |
| Qwen 3 32B | 21.4 GB | 70% on GPU ~5.0 tok/s | 70% on GPU ~4.3 tok/s |
| Llama 3.3 70B | 43.2 GB | 35% on GPU ~1.4 tok/s | 35% on GPU ~1.3 tok/s |
| gpt-oss-120b | 62.0 GB | Too small | Too small |
Verdict
Same 16 GB, so the same models fit. The 5060 Ti has 448 GB/s against 320 GB/s and the CUDA ecosystem, so it generates tokens roughly 50–60% faster and works with every AI tool out of the box. The 9060 XT is the cheaper way to get 16 GB and works well in llama.cpp, Ollama and LM Studio through Vulkan or ROCm.
Which should you buy?
Choose the RTX 5060 Ti 16GB if…
- You want every AI tool to work out of the box.
- You want faster token generation.
- You generate images in ComfyUI.
Choose the Radeon RX 9060 XT 16GB if…
- You want the cheapest new 16 GB card.
- You use LM Studio, Ollama or llama.cpp.
- Speed is less important than capacity.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | RTX 5060 Ti 16GB: best quant | Radeon RX 9060 XT 16GB: best quant | RTX 5060 Ti 16GB: max context | Radeon RX 9060 XT 16GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | Q8_0 | Q8_0 | 256K | 256K |
| Qwen 3 14B | Q6_K | Q6_K | 37K | 37K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 55K | 55K |
| Qwen 3.6 27B | IQ4_XS | IQ4_XS | – | – |
| Qwen 3 32B | Q2_K | Q2_K | – | – |
| Llama 3.3 70B | Does not fit | Does not fit | – | – |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | RTX 5060 Ti 16GB | Radeon RX 9060 XT 16GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP8 | FP8 |
| Qwen-Image | GGUF Q5_K_S | GGUF Q5_K_S |
| Wan 2.2 A14B (T2V / I2V) | GGUF Q5_K_S | GGUF Q5_K_S |
Frequently asked questions
Is the RTX 5060 Ti 16GB or Radeon RX 9060 XT 16GB better for local LLMs?
Same 16 GB, so the same models fit. The 5060 Ti has 448 GB/s against 320 GB/s and the CUDA ecosystem, so it generates tokens roughly 50–60% faster and works with every AI tool out of the box. The 9060 XT is the cheaper way to get 16 GB and works well in llama.cpp, Ollama and LM Studio through Vulkan or ROCm.
Which is faster for LLM inference?
The RTX 5060 Ti 16GB, by about 64% for token generation on models that fit both (about 53 vs 32 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
Neither: both have 16.0 GB usable, so they run the same models.