No? You can run any model that fits in its VRAM, and you can run larger models with layer/MoE offloading. Ask an AI what the best models you can run on that card are, then ask it for newer models than that. Ask what tuning options to pass to llama.cpp, and what the auto-tuning options are. Use ROCm builds.

It looks like your card has 16GB VRAM? Start with Qwen 3.5 9B Unsloth GGUFs (UD-Q6_K_XL) and branch out from there.

No, but yes? OmniCoder 9B at Q6 fits on my 9070 XT with 200k+ tokens of context, and it works pretty well with OpenCode. It is for sure the best local model that I've managed to squeeze onto my GPU, and it even works at 120k context at Q3 on an 8GB RX 580 GPU.

I can't imagine trying to using this model on either GPU for real work. I can use much bigger and faster models on the $3 Chutes subscription or $10 OpenCode Go subscription.

Even so, I am still excited. I don't feel like there was even a model worth using with a tool like OpenCode 6 to 9 months ago. I like the way things are heading, and I am looking forward to seeing how capable coding models of this size are in another 6 to 9 months!

Well, this specific solution was only set up on specific hardware, and is Nvidia dependent, as the readme stares.

That doesn’t mean the 9070XT can’t do AI stuff, quite the opposite. ROCm gets better all the time. There are many AI workloads you can do on AMD cards.

Is it a card I would choose if I was primarily working on AI? Absolutely not. But it is the card I own and it’s been a great value for gaming.