The Radeon RX 9060 XT 16GB has 16 GB of GDDR6 and 320 GB/s of bandwidth. That is enough memory to run 14B and 20B models, at moderate speed.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RX 9060 XT 16GB specs that matter for local AI
- Memory: 16 GB GDDR6 on a 128-bit bus
- Memory bandwidth: 320 GB/s
- Board power: 150 to 182 W depending on the model
VRAM decides which models fit. Bandwidth decides how fast they generate text, because every new token means reading the whole model from memory.
Which AI models fit on the RX 9060 XT 16GB?
The table assumes Q4_K_M quantization (about 4.9 bits per weight) and an 8K context. Totals include the KV cache and about 1 GB of runtime overhead. Speed is an estimate: memory bandwidth divided by model size, at 60 to 80 percent efficiency, which matches published llama.cpp results on several RTX cards.
| Model | Weights | KV cache (8K) | Total (est.) | On 16 GB | Speed (est.) |
|---|---|---|---|---|---|
| Qwen3 4B | 2.5 GB | 1.2 GB | 4.7 GB | Fits | 78 to 104 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 39 to 52 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 38 to 51 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 26 to 34 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Fits | 21 to 28 tok/s |
| OpenAI gpt-oss-20b | 12.8 GB | 0.2 GB | 14.0 GB | Fits | Varies (MoE) |
| Mistral Small 3.2 24B | 14.7 GB | 1.3 GB | 17.0 GB | No | – |
| Qwen3.6 27B | 17.0 GB | 0.5 GB | 18.6 GB | No | – |
| Gemma 3 27B Instruct | 16.8 GB | 1.1 GB | 18.9 GB | No | – |
| Qwen3 32B | 20.1 GB | 2.1 GB | 23.2 GB | No | – |
| Qwen3.6 35B-A3B | 22.0 GB | 0.2 GB | 23.2 GB | No | – |
“Fits” means the estimate is at most 92 percent of VRAM. “Tight” means it is between 92 and 100 percent, so close other GPU apps. Mixture-of-Experts models (gpt-oss-20b and Qwen3.6 35B-A3B) need their full weights in memory, but they only use part of them per token, so speed is not estimated.
Best models for the RX 9060 XT 16GB
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K.
- gpt-oss-20b: about 14 GB.
- 8B and 12B models: comfortable at Q4 or Q8.
How context length changes things
The KV cache grows with every token of context, so a model that fits at 4K can fail at 32K.
| Model (Q4_K_M) | Context | KV cache | Total (est.) | On 16 GB |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | 4K | 0.54 GB | 6.5 GB | Fits |
| Llama 3.1 8B Instruct | 8K | 1.07 GB | 7.0 GB | Fits |
| Llama 3.1 8B Instruct | 16K | 2.15 GB | 8.1 GB | Fits |
| Llama 3.1 8B Instruct | 32K | 4.29 GB | 10.2 GB | Fits |
| Qwen3 14B | 4K | 0.67 GB | 10.7 GB | Fits |
| Qwen3 14B | 8K | 1.34 GB | 11.4 GB | Fits |
| Qwen3 14B | 16K | 2.68 GB | 12.7 GB | Fits |
| Qwen3 14B | 32K | 5.37 GB | 15.4 GB | Tight |
A Q8 KV cache (an option in Ollama and llama.cpp) roughly halves the cache cost if you need more context.
What does not fit
24B to 32B models need 17 to 23 GB at Q4, more than 16 GB.
Capacity at moderate speed. It gives 16 GB of VRAM, and a 14B model runs at about 21 to 28 tokens per second. Faster cards with the same memory exist if speed matters.
Verdict
What speed to expect
At 320 GB/s, an 8B Q4 model has a ceiling near 65 tokens per second. Applying the 60 to 80 percent efficiency range seen in published llama.cpp tests gives about 39 to 52 tokens per second for Llama 3.1 8B and 26 to 34 for Gemma 3 12B. We did not find a reliable measured result for this exact card, so these are estimates that could be off by 10 to 20 percent. Speed on AMD also depends on the backend (HIP or Vulkan).
Frequently asked questions
Can the RX 9060 XT 16GB run a 14B model?
Yes, about 11.4 GB at Q4_K_M with 8K context.
Can it run gpt-oss-20b?
Yes, about 14 GB.
Does AMD work for local AI?
Yes, llama.cpp supports AMD GPUs through HIP and Vulkan.
Sources and method
- RX 9060 XT 16GB specs: TechSpot’s launch article (16 GB, 320 GB/s).
- Linear-attention and sliding-window layers are handled with reduced KV cache; small fixed states in some newer models are not counted.
- Model architecture: each model’s published
config.jsonon Hugging Face (Llama 3.1 8B and Gemma 3 are read from the unsloth mirrors because the original repositories require a login). - VRAM and speed figures are estimates from our calculator, not our own benchmarks. Last updated 3 Nov 2026.
