Radeon RX 9060 XT 16 GB
Radeon RX 9060 XT 16 GB
The Radeon RX 9060 XT 16 GB is an Amd GPU built on the RDNA 4.0 architecture, released 2025-06-04. For running AI locally, the numbers that matter are its 16 GB of GDDR6 and 322.3 GB/s of memory bandwidth. VRAM decides which models fit at all; bandwidth sets how fast they generate text.
What you can run on 16 GB
At Q4_K_M quantization (the usual local default), 16 GB holds models up to roughly 25B parameters, leaving headroom for context. On this card you can run, among others:
- gpt-oss-20b - 21.5B parameters
- Qwen2.5-14B-Instruct - 14.8B parameters
- Qwen2.5-Coder-14B-Instruct - 14.8B parameters
- Phi-4 - 14B parameters
- Qwen3 14B - 14B parameters
Larger models need a higher-VRAM card, a second GPU, or CPU offload (which is much slower). Check any specific model with the VRAM calculator, or see the full picture on what can I run.
Local LLM speed (LLaMA 3, llama.cpp)
Single-stream token-generation throughput - estimated from memory bandwidth:
| Model (quant) | Speed on Radeon RX 9060 XT 16 GB |
|---|---|
| Llama 3 8B (Q4_K_M) | 37.7 tok/s |
| Llama 3 8B (F16) | ✗ won't fit |
| Llama 3 70B (Q4_K_M) | ✗ won't fit |
Because decode is memory-bandwidth bound, the 322.3 GB/s figure is the best single predictor of chat speed on this card. Estimates are calibrated against measured RTX-40-series cards and are typically within ~15%.
Memory and power
- VRAM: 16 GB GDDR6 (128-bit bus)
- Bandwidth: 322.3 GB/s
- TDP: 160 W - a 450 W+ power supply is recommended
- Process: 4 nm
- Interface: PCIe 5.0 x16
Quantization and context
Quantization trades a little quality for a lot of VRAM. On 16 GB you can fit roughly a 25B model at Q4_K_M, about a 13B model at the higher-quality Q8, or a smaller model at full FP16. Longer context windows also consume VRAM (the KV cache grows with context length), so leave a few GB of headroom if you plan to use large prompts or many concurrent requests. For most chat and coding use, Q4_K_M on this card is the sweet spot between speed, quality, and the 16 GB budget.
How it compares
Similar cards for local AI, by VRAM and 8B-Q4 speed:
| GPU | VRAM | Bandwidth | Llama 3 8B Q4 |
|---|---|---|---|
| Radeon RX 9060 XT 16 GB | 16 GB | 322.3 GB/s | 37.7 tok/s |
| AMD Radeon RX 7600 XT | 16 GB | 288 GB/s | 33.7 tok/s |
| AMD Radeon RX 7800 XT | 16 GB | 624 GB/s | 73 tok/s |
| AMD Radeon RX 9070 | 16 GB | 512 GB/s | 59.9 tok/s |
Bottom line
The Radeon RX 9060 XT 16 GB is best for llm-inference, image-gen. Its 16 GB suits 7B-14B models and image generation well. If you need more, compare with AMD Radeon RX 7600 XT and AMD Radeon RX 7800 XT.
Sources
- Specifications: RightNow GPU Database (TechPowerUp data)
- Benchmarks: GPU-Benchmarks-on-LLM-Inference (basis for the bandwidth estimate)
Specs and benchmarks last checked 2026-06-08. Verify current pricing before buying.
Frequently asked
Quick answers to common questions
How much VRAM does the Radeon RX 9060 XT 16 GB have?
The Radeon RX 9060 XT 16 GB has 16 GB of VRAM with 322.3 GB/s memory bandwidth.
What local AI models can run on the Radeon RX 9060 XT 16 GB?
The Radeon RX 9060 XT 16 GB with 16 GB VRAM can run many models depending on quantization. Models up to ~24B params may fit at Q4_K_M. Use our VRAM calculator to check specific models.
Is the Radeon RX 9060 XT 16 GB good for local AI inference?
Radeon RX 9060 XT 16 GB is best for llm-inference, image-gen. With ample VRAM it handles most open models well.
Where can I buy the Radeon RX 9060 XT 16 GB?
Check our buy links above for the best current prices on Amazon, Newegg, and B&H. Prices vary by retailer and availability.
How does the Radeon RX 9060 XT 16 GB compare to other GPUs?
Radeon RX 9060 XT 16 GB has 16 GB VRAM and 322.3 GB/s bandwidth. It is a mid-to-high-range card capable of running most 7B–30B models. Browse our hardware directory for side-by-side comparisons.
What power supply do I need for the Radeon RX 9060 XT 16 GB?
The Radeon RX 9060 XT 16 GB has a TDP of 160W. A standard quality PSU of 650W+ should suffice. Always check the manufacturer's recommendations for your specific build.
Nearby options
Similar hardware and models that fit
Similar hardware
Comments coming soon
Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.