The search for a 32-gigabyte GPU that does not require a second mortgage has led me to the ASUS Turbo Radeon AI Pro R9700. While this card sits at a higher price point than some entry-level options, currently retailing for approximately $2,000 (compensated affiliate link), it remains a “relatively” obtainable choice for those prioritizing local AI performance.
The card features an AMD R9700 GPU and 32 GB of onboard memory, a capacity that has emerged as a practical baseline for running local language models, video generation, and image diffusion.
See it in action in my latest review!
ASUS recommends a 750-watt power supply for this hardware. The unit features a single power connector, which required an adapter for my specific setup. Its memory bandwidth is rated at around 640 gigabytes per second, putting it behind more expensive Nvidia cards but is “good enough” for fast local token generation.
To test the accessibility of this card, I installed it in a Lenovo Legion 7 Tower from roughly 2021. This system uses DDR4 memory and PCIe 3.0, representing the type of older hardware many users might already own. The goal was to see if the 32 GB of VRAM could compensate for an older motherboard and processor. By loading models entirely into the GPU memory, the performance drop-off typically associated with older systems was largely mitigated.
Testing began with the Qwen 3.8-27B model using approximately 98,000 tokens of context. With Multi-Token Prediction (MTP) enabled, the card generated text at a rate of 42.5 tokens per second in a real-world example. In a more intensive analytical task, I processed a 69-page FCC filing totaling about 50,000 tokens. The prefill process took just over 90 seconds, and the model subsequently provided answers at a rate of 55 tokens per second.
Beyond text, the card was tested for image and video generation with ComfyUI. Using the Minimax H3 model, a five-second video at 480p resolution was generated in approximately three and a half minutes. For image generation, the Flux 2 Klein 9B model produced a 1024×1024 image in about 90 seconds on the first pass, with subsequent generations taking approximately 20 seconds once the model was fully loaded in memory.
The hardware also serves as a capable gaming GPU. In No Man’s Sky at 4K resolution with ultra settings, the card maintained frame rates between 70 and 75 frames per second. On the 3D Mark Time Spy benchmark, it achieved a score of 23,554, outperforming the RTX 3080 that shipped with the test PC.
This card represents a middle ground for users who need significant VRAM but cannot justify the close to five-figure investment required for professional-grade Nvidia silicon. It offers enough headroom to handle maturing AI models that require specific hardware optimizations while remaining compatible with older PC builds. Finding a balance between cost, memory capacity, and speed is the central challenge of building a local AI workstation, and this hardware provides a functional path forward for those working within a $2,000 budget.
Be sure to check out more of my local AI series and see some more affordable options!