What AI models can AMD Radeon RX 7900 XTX run?

The AMD Radeon RX 7900 XTX can run 63 AI models. Top performers include Gemma 4 26B-A4B, Llama 3.2 1B Instruct, Arcee Trinity Nano 6B. See the full compatibility table above for speeds and quality ratings.

Is AMD Radeon RX 7900 XTX good for AI coding?

Yes. With 24 GB, the AMD Radeon RX 7900 XTX supports the Power AI Coding tier: large coding models at good quality.

How much VRAM does AMD Radeon RX 7900 XTX have?

The AMD Radeon RX 7900 XTX has 24 GB of GDDR6 VRAM with 960 GB/s bandwidth.

Can AMD Radeon RX 7900 XTX run 70B models?

70B models can run on the AMD Radeon RX 7900 XTX with CPU offloading, but performance will be reduced. Consider a device with 48GB+ inference memory for full-speed 70B inference.

Is AMD Radeon RX 7900 XTX worth it for AI?

At $849, the AMD Radeon RX 7900 XTX offers 24 GB GDDR6 VRAM and runs 63 AI models. It handles local AI inference well.

Desktop GPU

AMD Radeon RX 7900 XTX

24 GB GDDR6 · 960 GB/s

From

$849

Estimated street price

VRAM

24 GB

Bandwidth

960 GB/s

TDP

355W

Models

Tier

Power

The AMD Radeon RX 7900 XTX with 24 GB GDDR6 VRAM can handle 63 AI models across embedding, ai_building, coding. Best performance: Gemma 4 26B-A4B at 218 tok/s (excellent). For AI coding workflows, it supports the Power AI Coding tier, running 32B coding models at good quality. Current price: approximately $849.

Source: OwnRig methodology

VRAM

24 GB

Bandwidth

960 GB/s

Memory Type

GDDR6

TDP

355W

Form Factor

3-slot, 287mm

Builder Capability: Power AI Coding

Runs 32B coding models at good quality. Can handle coding model + embeddings concurrently.

Software

Inference Backends

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

ROCm

stable

Best AMD path for PyTorch, llama.cpp, and vLLM workloads on RDNA 3.

Vulkan

stable

Reliable fallback backend in llama.cpp when ROCm support is incomplete.

What it can run

63 models


all-MiniLM-L6-v2	FP16	–	Excellent
Arcee Trinity Mini 26B	Q5_K_M	59 tok/s	Excellent
Arcee Trinity Nano 6B	Q8_0	169 tok/s	Excellent
Code Llama 34B Instruct	Q4_K_M	19 tok/s	Good
Codestral 22B	Q5_K_M	30 tok/s	Good
Command R 35B	Q4_K_M	–	Acceptable
DeepSeek Coder V2 Lite 16B	Q5_K_M	47 tok/s	Good
DeepSeek R1	Q2_K	–	Not viable
DeepSeek R1 Distill Qwen 32B	Q4_K_M	21 tok/s	Good
DeepSeek R1 Distill Qwen 7B	Q4_K_M	79 tok/s	Good
DeepSeek V3	Q2_K	–	Not viable
FLUX.1 Dev	Q8_0	–	Good
Gemma 2 27B Instruct	Q4_K_M	19 tok/s	Good
Gemma 2 9B Instruct	Q8_0	69 tok/s	Good
Gemma 3 12B	Q5_K_M	64 tok/s	Good
Gemma 3 27B	Q4_K_M	15 tok/s	Good
Gemma 3 4B	Q8_0	–	Good
Gemma 4 26B-A4B	Q5_K_M	218 tok/s	Excellent
Gemma 4 31B	Q4_K_M	36 tok/s	Good
Gemma 4 E2B	Q8_0	144 tok/s	Excellent
Gemma 4 E4B	Q8_0	89 tok/s	Excellent
GigaChat Lightning 10B	Q8_0	57 tok/s	Good
InternLM 2.5 7B Chat	Q8_0	76 tok/s	Good
Llama 3.1 70B Instruct	Q2_K	1 tok/s	Marginal
Llama 3.1 8B Instruct	Q8_0	60 tok/s	Good
Llama 3.2 11B Vision	Q8_0	82 tok/s	Good
Llama 3.2 1B Instruct	Q8_0	189 tok/s	Good
Llama 3.2 3B Instruct	Q8_0	129 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	Q5_K_M	26 tok/s	Good
Mistral 7B Instruct v0.3	Q8_0	77 tok/s	Good
Mistral Large 2 123B	Q2_K	–	Not viable
Mistral Small 24B Instruct	Q5_K_M	28 tok/s	Good
Mixtral 8x7B Instruct	Q3_K_M	30 tok/s	Good
nomic-embed-text v1.5	FP16	–	Good
NVIDIA Nemotron-3-super-120B-A12B	Q2_K	–	Not viable
Phi-3 Medium 14B Instruct	Q8_0	47 tok/s	Good
Phi-3 Mini 3.8B Instruct	Q8_0	112 tok/s	Good
Phi-4 14B	Q5_K_M	50 tok/s	Good
Phi-4 Mini	Q8_0	120 tok/s	Good
Qwen 2.5 14B Instruct	Q5_K_M	47 tok/s	Good
Qwen 2.5 72B Instruct	Q2_K	2 tok/s	Marginal
Qwen 2.5 7B Instruct	Q8_0	76 tok/s	Good
Qwen 2.5 Coder 32B Instruct	Q4_K_M	15 tok/s	Good
Qwen 2.5 Coder 7B Instruct	Q8_0	77 tok/s	Good
Qwen3-14B Instruct	Q8_0	35 tok/s	Good
Qwen3-30B-A3B	Q5_K_M	22 tok/s	Good
Qwen3-32B Instruct	Q5_K_M	17 tok/s	Acceptable
Qwen3-8B Instruct	Q8_0	71 tok/s	Good
Qwen3.5-122B-A10B	Q3_K_M	3 tok/s	Marginal
Qwen3.5-27B	Q5_K_M	21 tok/s	Good
Qwen3.5-397B (MoE)	Q2_K	–	Not viable
Qwen3.6-27B	Q5_K_M	21 tok/s	Good
Qwen3.6-35B-A3B	Q4_K_M	22 tok/s	Good
QwQ 32B Preview	Q4_K_M	21 tok/s	Good
Stable Diffusion 3 Medium	FP16	–	Good
Stable Diffusion 3.5 Large	FP16	–	Good
Stable Diffusion XL 1.0	FP16	–	Excellent
StarCoder 2 15B	Q8_0	43 tok/s	Good
Whisper Large V3	FP16	–	Excellent
Whisper Large V3 Turbo	FP16	–	Good
Yi 1.5 34B Chat	Q4_K_M	–	Acceptable

Showing 63 of 63 entries

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FAQ

Frequently Asked Questions

What AI models can AMD Radeon RX 7900 XTX run?: The AMD Radeon RX 7900 XTX can run 63 AI models. Top performers include Gemma 4 26B-A4B, Llama 3.2 1B Instruct, Arcee Trinity Nano 6B. See the full compatibility table above for speeds and quality ratings.
Is AMD Radeon RX 7900 XTX good for AI coding?: Yes. With 24 GB, the AMD Radeon RX 7900 XTX supports the Power AI Coding tier: large coding models at good quality.
How much VRAM does AMD Radeon RX 7900 XTX have?: The AMD Radeon RX 7900 XTX has 24 GB of GDDR6 VRAM with 960 GB/s bandwidth.
Can AMD Radeon RX 7900 XTX run 70B models?: 70B models can run on the AMD Radeon RX 7900 XTX with CPU offloading, but performance will be reduced. Consider a device with 48GB+ inference memory for full-speed 70B inference.
Is AMD Radeon RX 7900 XTX worth it for AI?: At $849, the AMD Radeon RX 7900 XTX offers 24 GB GDDR6 VRAM and runs 63 AI models. It handles local AI inference well.

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