What AI models can AMD Radeon RX 9060 XT 16GB run?

The AMD Radeon RX 9060 XT 16GB can run 62 AI models. Top performers include Gemma 4 26B-A4B, Llama 3.2 1B Instruct, DeepSeek R1 Distill Qwen 7B. See the full compatibility table above for speeds and quality ratings.

Is AMD Radeon RX 9060 XT 16GB good for AI coding?

Yes. With 16 GB, the AMD Radeon RX 9060 XT 16GB handles single-model coding workflows well at the Capable tier.

How much VRAM does AMD Radeon RX 9060 XT 16GB have?

The AMD Radeon RX 9060 XT 16GB has 16 GB of GDDR6 VRAM with 320 GB/s bandwidth.

Can AMD Radeon RX 9060 XT 16GB run 70B models?

70B models can run on the AMD Radeon RX 9060 XT 16GB with CPU offloading, but performance will be reduced. Consider a device with 48GB+ inference memory for full-speed 70B inference.

Is AMD Radeon RX 9060 XT 16GB worth it for AI?

At $349, the AMD Radeon RX 9060 XT 16GB offers 16 GB GDDR6 VRAM and runs 62 AI models. It works for smaller models and experimentation.

Desktop GPU

AMD Radeon RX 9060 XT 16GB

16 GB GDDR6 · 320 GB/s

From

$349

Estimated street price

VRAM

16 GB

Bandwidth

320 GB/s

TDP

150W

Models

Tier

Capable

The AMD Radeon RX 9060 XT 16GB with 16 GB GDDR6 VRAM can handle 62 AI models across embedding, ai_building, coding. Best performance: Gemma 4 26B-A4B at 109 tok/s (excellent). For AI coding workflows, it supports the Capable AI Coding tier, handling single model workflows well. Current price: approximately $349.

Source: OwnRig methodology

VRAM

16 GB

Bandwidth

320 GB/s

Memory Type

GDDR6

TDP

150W

Form Factor

2-slot, 240mm

Builder Capability: Capable AI Coding

Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.

Software

Inference Backends

The software stacks that matter most for real-world inference on this device.

ROCm

beta

Newer RDNA 4 ROCm path with improving runtime support but less field maturity than CUDA.

Vulkan

stable

Most reliable llama.cpp path for local inference on early RDNA 4 cards.

What it can run

62 models


all-MiniLM-L6-v2	FP16	–	Excellent
Arcee Trinity Mini 26B	Q3_K_M	30 tok/s	Good
Arcee Trinity Nano 6B	Q8_0	56 tok/s	Excellent
Code Llama 34B Instruct	Q2_K	–	Acceptable
Codestral 22B	Q3_K_M	16 tok/s	Acceptable
Command R 35B	Q3_K_M	2 tok/s	Marginal
DeepSeek Coder V2 Lite 16B	Q5_K_M	44 tok/s	Good
DeepSeek R1	Q2_K	–	Not viable
DeepSeek R1 Distill Qwen 32B	Q3_K_M	4 tok/s	Marginal
DeepSeek R1 Distill Qwen 7B	Q8_0	75 tok/s	Good
DeepSeek V3	Q2_K	–	Not viable
FLUX.1 Dev	Q4_K_M	–	Acceptable
Gemma 2 27B Instruct	Q3_K_M	11 tok/s	Acceptable
Gemma 2 9B Instruct	Q8_0	–	Acceptable
Gemma 3 12B	Q5_K_M	37 tok/s	Acceptable
Gemma 3 27B	Q3_K_M	5 tok/s	Marginal
Gemma 3 4B	Q5_K_M	17 tok/s	Acceptable
Gemma 4 26B-A4B	Q3_K_M	109 tok/s	Excellent
Gemma 4 31B	Q3_K_M	7 tok/s	Marginal
Gemma 4 E2B	Q8_0	48 tok/s	Good
Gemma 4 E4B	Q8_0	29 tok/s	Acceptable
GigaChat Lightning 10B	Q8_0	48 tok/s	Acceptable
InternLM 2.5 7B Chat	Q8_0	–	Acceptable
Llama 3.1 70B Instruct	Q2_K	–	Not viable
Llama 3.1 8B Instruct	Q8_0	48 tok/s	Good
Llama 3.2 11B Vision	Q6_K	33 tok/s	Acceptable
Llama 3.2 1B Instruct	Q8_0	106 tok/s	Good
Llama 3.2 3B Instruct	Q8_0	66 tok/s	Good
Llama 3.3 70B Instruct	Q3_K_M	–	Not viable
Llama 4 Scout	Q3_K_M	–	Not viable
LLaVA 1.6 13B	Q4_K_M	19 tok/s	Acceptable
Mistral 7B Instruct v0.3	Q8_0	–	Acceptable
Mistral Large 2 123B	Q2_K	–	Not viable
Mistral Small 24B Instruct	Q3_K_M	16 tok/s	Acceptable
Mixtral 8x7B Instruct	Q2_K	2 tok/s	Marginal
nomic-embed-text v1.5	FP16	–	Acceptable
NVIDIA Nemotron-3-super-120B-A12B	Q2_K	–	Not viable
Phi-3 Medium 14B Instruct	Q5_K_M	25 tok/s	Acceptable
Phi-3 Mini 3.8B Instruct	Q8_0	–	Acceptable
Phi-4 14B	Q4_K_M	25 tok/s	Acceptable
Phi-4 Mini	Q8_0	60 tok/s	Good
Qwen 2.5 14B Instruct	Q4_K_M	26 tok/s	Acceptable
Qwen 2.5 72B Instruct	Q2_K	–	Not viable
Qwen 2.5 7B Instruct	Q8_0	–	Acceptable
Qwen 2.5 Coder 32B Instruct	Q2_K	9 tok/s	Acceptable
Qwen 2.5 Coder 7B Instruct	Q5_K_M	46 tok/s	Good
Qwen3-14B Instruct	Q5_K_M	14 tok/s	Acceptable
Qwen3-30B-A3B	Q3_K_M	–	Marginal
Qwen3-32B Instruct	Q3_K_M	2 tok/s	Marginal
Qwen3-8B Instruct	Q8_0	–	Acceptable
Qwen3.5-122B-A10B	Q3_K_M	–	Not viable
Qwen3.5-27B	Q3_K_M	22 tok/s	Acceptable
Qwen3.5-397B (MoE)	Q2_K	–	Not viable
Qwen3.6-35B-A3B	Q3_K_M	–	Marginal
QwQ 32B Preview	Q2_K	–	Acceptable
Stable Diffusion 3 Medium	FP16	–	Acceptable
Stable Diffusion 3.5 Large	FP16	–	Acceptable
Stable Diffusion XL 1.0	FP16	–	Excellent
StarCoder 2 15B	Q5_K_M	22 tok/s	Acceptable
Whisper Large V3	FP16	–	Excellent
Whisper Large V3 Turbo	FP16	–	Good
Yi 1.5 34B Chat	Q3_K_M	2 tok/s	Marginal

Showing 62 of 62 entries

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FAQ

Frequently Asked Questions

What AI models can AMD Radeon RX 9060 XT 16GB run?: The AMD Radeon RX 9060 XT 16GB can run 62 AI models. Top performers include Gemma 4 26B-A4B, Llama 3.2 1B Instruct, DeepSeek R1 Distill Qwen 7B. See the full compatibility table above for speeds and quality ratings.
Is AMD Radeon RX 9060 XT 16GB good for AI coding?: Yes. With 16 GB, the AMD Radeon RX 9060 XT 16GB handles single-model coding workflows well at the Capable tier.
How much VRAM does AMD Radeon RX 9060 XT 16GB have?: The AMD Radeon RX 9060 XT 16GB has 16 GB of GDDR6 VRAM with 320 GB/s bandwidth.
Can AMD Radeon RX 9060 XT 16GB run 70B models?: 70B models can run on the AMD Radeon RX 9060 XT 16GB with CPU offloading, but performance will be reduced. Consider a device with 48GB+ inference memory for full-speed 70B inference.
Is AMD Radeon RX 9060 XT 16GB worth it for AI?: At $349, the AMD Radeon RX 9060 XT 16GB offers 16 GB GDDR6 VRAM and runs 62 AI models. It works for smaller models and experimentation.

Own this GPU?

See every AI model it supports, expected performance, and how to build around it.

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