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AMD Radeon RX 9070 XT for local LLMs

Written by Jakub Rusinowski · Last updated

With 16 GB at 640 GB/s, the RX 9070 XT runs 75 catalogued models at Q4_K_M with 8K context. The largest that fits is OLMo 2 13B Instruct (~15.8 GB), and the top pick is EuroLLM 22B at 18–37 tok/s.

AMD's newest RDNA 4 architecture (2025). Excellent ROCm + Vulkan support. 16 GB at 640 GB/s — more memory and more bandwidth than the RTX 4070 Ti. Great value for AMD fans.

Models that run on the RX 9070 XT

Q4_K_M, 8K context, 16 GB usable. Ranked by quality and speed.

ModelVRAMSpeed
EuroLLM 22B
EuroLLM
14.1 GB18–37 tok/s
GPT-OSS 20B
GPT-OSS
13.8 GB70–146 tok/s
InternLM 3 20B Instruct
InternLM 3
12.9 GB19–40 tok/s
Cosmos 3 Nano
Cosmos 3
10.5 GB37–77 tok/s
StarCoder 2 15B
StarCoder 2
10.8 GB25–52 tok/s
Qwen 3 14B
Qwen 3
11.1 GB25–51 tok/s
Phi-4 (14B)
Phi-4 Family
10.9 GB25–52 tok/s
Cogito v1 14B
Cogito v1
10.9 GB25–52 tok/s
Ministral 3 14B
Ministral 3
9.3 GB26–53 tok/s
DeepSeek R1 Distill Qwen 14B
DeepSeek R1
10.6 GB26–53 tok/s
Gemma 4 12B (Unified)
Gemma 4
8 GB29–60 tok/s
Bielik PL 11B v3.0 Instruct
Bielik
7.4 GB31–64 tok/s
Showing 12 of 75

Buy it or rent the same memory

Buy the card, or rent a GPU with the same memory by the hour to try models first.

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AMD Radeon RX 9070 XT 16GB
16 GB VRAM · 304 W board power
2026 prices are volatile — check the current listing.

Speed vs other GPUs

Llama 3.1 8B, Q4_K_M. Estimated ranges. How this is calculated

Specifications

Specs last updated 2026-09-29.

Memory
16 GB
Memory bandwidth
640 GB/s
Architecture
RDNA 4 Navi 48
Series
RX 9000-series
Board power
304 W
Release year
2025
Launch price
$599
Usable for models
16 GB
Best for14B modelsAMD 2025 flagshipefficient 16GB
Recommended first model (Ollama)

Similar GPUs

Frequently asked questions

Can the AMD Radeon RX 9070 XT run local LLMs?

Yes. With 16 GB (16 GB usable by a model) it runs 75 of the catalogued models at Q4_K_M with 8K context; the largest is OLMo 2 13B Instruct, needing about 15.8 GB.

How fast is the AMD Radeon RX 9070 XT for AI inference?

It is estimated to run Llama 3.1 8B at 42–87 tok/s at Q4_K_M. Llama 3.3 70B does not fit: it needs about 44 GB against 16 GB usable. These are modelled estimates from memory bandwidth, not measurements; the methodology page shows the formula.

What LLMs can I run on 16 GB?

Among the best that fit: EuroLLM 22B, GPT-OSS 20B, InternLM 3 20B Instruct, Cosmos 3 Nano, StarCoder 2 15B. The quickest start is Ollama: ollama run qwen3:14b.