What AI models can AMD Radeon RX 9070 run?

The AMD Radeon RX 9070 can run 63 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 9070 good for AI coding?

Yes. With 16 GB, the AMD Radeon RX 9070 handles single-model coding workflows well at the Capable tier.

How much VRAM does AMD Radeon RX 9070 have?

The AMD Radeon RX 9070 has 16 GB of GDDR6 VRAM with 640 GB/s bandwidth.

Can AMD Radeon RX 9070 run 70B models?

70B models can run on the AMD Radeon RX 9070 with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.

Is AMD Radeon RX 9070 worth it for AI?

At $549, the AMD Radeon RX 9070 offers 16 GB VRAM and runs 63 AI models. It works for smaller models and experimentation.

Desktop GPU

AMD Radeon RX 9070

16 GB GDDR6 · 640 GB/s

From

$549

Estimated street price

VRAM

16 GB

Bandwidth

640 GB/s

TDP

180W

Models

Tier

Capable

The AMD Radeon RX 9070 with 16 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 Capable AI Coding tier, handling single model workflows well. Current price: approximately $549.

Source: OwnRig methodology

VRAM

16 GB

Bandwidth

640 GB/s

Memory Type

GDDR6

TDP

180W

Form Factor

2-slot, 270mm

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

RDNA 4 ROCm support is improving rapidly. Vulkan is the safer default in llama.cpp until ROCm matures further for this architecture.

Vulkan

stable

Most reliable llama.cpp backend for local inference on RDNA 4. Recommended until ROCm 7.x fully stabilises for Navi 48.

What it can run

63 models


all-MiniLM-L6-v2	FP16	–	Excellent
Arcee Trinity Mini 26B	Q3_K_M	60 tok/s	Good
Arcee Trinity Nano 6B	Q8_0	112 tok/s	Excellent
Code Llama 34B Instruct	Q2_K	–	Acceptable
Codestral 22B	Q3_K_M	32 tok/s	Good
Command R 35B	Q3_K_M	4 tok/s	Marginal
DeepSeek Coder V2 Lite 16B	Q5_K_M	88 tok/s	Good
DeepSeek R1	Q2_K	–	Not viable
DeepSeek R1 Distill Qwen 32B	Q3_K_M	8 tok/s	Marginal
DeepSeek R1 Distill Qwen 7B	Q8_0	150 tok/s	Good
DeepSeek V3	Q2_K	–	Not viable
FLUX.1 Dev	Q4_K_M	–	Acceptable
Gemma 2 27B Instruct	Q3_K_M	22 tok/s	Acceptable
Gemma 2 9B Instruct	Q8_0	–	Acceptable
Gemma 3 12B	Q5_K_M	74 tok/s	Good
Gemma 3 27B	Q3_K_M	10 tok/s	Acceptable
Gemma 3 4B	Q5_K_M	34 tok/s	Good
Gemma 4 26B-A4B	Q3_K_M	218 tok/s	Excellent
Gemma 4 31B	Q3_K_M	14 tok/s	Acceptable
Gemma 4 E2B	Q8_0	96 tok/s	Good
Gemma 4 E4B	Q8_0	58 tok/s	Good
GigaChat Lightning 10B	Q8_0	96 tok/s	Good
InternLM 2.5 7B Chat	Q8_0	–	Acceptable
Llama 3.1 70B Instruct	Q2_K	–	Not viable
Llama 3.1 8B Instruct	Q8_0	96 tok/s	Good
Llama 3.2 11B Vision	Q6_K	66 tok/s	Good
Llama 3.2 1B Instruct	Q8_0	212 tok/s	Good
Llama 3.2 3B Instruct	Q8_0	132 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	38 tok/s	Good
Mistral 7B Instruct v0.3	Q8_0	–	Acceptable
Mistral Large 2 123B	Q2_K	–	Not viable
Mistral Small 24B Instruct	Q3_K_M	32 tok/s	Good
Mixtral 8x7B Instruct	Q2_K	4 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	50 tok/s	Good
Phi-3 Mini 3.8B Instruct	Q8_0	–	Acceptable
Phi-4 14B	Q4_K_M	50 tok/s	Good
Phi-4 Mini	Q8_0	120 tok/s	Good
Qwen 2.5 14B Instruct	Q4_K_M	52 tok/s	Good
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	18 tok/s	Acceptable
Qwen 2.5 Coder 7B Instruct	Q5_K_M	92 tok/s	Good
Qwen3-14B Instruct	Q5_K_M	28 tok/s	Good
Qwen3-30B-A3B	Q3_K_M	–	Marginal
Qwen3-32B Instruct	Q3_K_M	4 tok/s	Marginal
Qwen3-8B Instruct	Q8_0	–	Acceptable
Qwen3.5-122B-A10B	Q3_K_M	–	Not viable
Qwen3.5-27B	Q3_K_M	44 tok/s	Good
Qwen3.5-397B (MoE)	Q2_K	–	Not viable
Qwen3.6-27B	Q3_K_M	44 tok/s	Good
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	44 tok/s	Good
Whisper Large V3	FP16	–	Excellent
Whisper Large V3 Turbo	FP16	–	Good
Yi 1.5 34B Chat	Q3_K_M	4 tok/s	Marginal

Showing 63 of 63 entries

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FAQ

Frequently Asked Questions

What AI models can AMD Radeon RX 9070 run?: The AMD Radeon RX 9070 can run 63 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 9070 good for AI coding?: Yes. With 16 GB, the AMD Radeon RX 9070 handles single-model coding workflows well at the Capable tier.
How much VRAM does AMD Radeon RX 9070 have?: The AMD Radeon RX 9070 has 16 GB of GDDR6 VRAM with 640 GB/s bandwidth.
Can AMD Radeon RX 9070 run 70B models?: 70B models can run on the AMD Radeon RX 9070 with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
Is AMD Radeon RX 9070 worth it for AI?: At $549, the AMD Radeon RX 9070 offers 16 GB VRAM and runs 63 AI models. It works for smaller models and experimentation.

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