RTX 2070 Super vs. RX 7600: Which GPU Delivers Better AI Image Generation for Beginners?

RTX 2070 Super vs. RX 7600: Which GPU Delivers Better AI Image Generation for Beginners?

As AI-powered image generation tools like Stable Diffusion and ComfyUI gain traction among hobbyists and creators, hardware compatibility has become a critical consideration. A recent Reddit post from user /u/raupi12, posted in the r/StableDiffusion community, highlights a common dilemma: choosing between an older NVIDIA RTX 2070 Super and a newer AMD RX 7600 — both with 8GB of VRAM — for testing AI image generation on an AMD AM4 system equipped with a Ryzen 5700G CPU and 32GB of system RAM.

According to community responses and industry benchmarks, the RTX 2070 Super remains the superior choice for AI image generation despite its age, primarily due to NVIDIA’s robust CUDA and Tensor Core support. While the RX 7600 offers better raw gaming performance and newer architecture, AMD’s ROCm stack — required for running Stable Diffusion on AMD GPUs — lacks broad software compatibility, especially with user-friendly interfaces like ComfyUI. Most Stable Diffusion models and extensions are optimized for NVIDIA’s ecosystem, and many users report installation failures or severe performance bottlenecks when attempting to run ROCm on non-server-grade AMD hardware.

System requirements for AI image generation are often misunderstood. While 32GB of system RAM is more than sufficient, the 8GB VRAM on both GPUs is borderline for modern models. For beginners, models like SD 1.5 (Stable Diffusion 1.5) or TinySD are ideal starting points, as they require as little as 4–6GB VRAM and produce high-quality results on modest hardware. Larger models such as SDXL 1.0 or SDXL-Turbo may struggle or crash entirely on 8GB VRAM, especially when using higher resolutions or multiple negative prompts. Users are advised to start with 512x512 resolution outputs and gradually increase only after confirming stability.

Additionally, the user’s existing AMD Ryzen 5700G — which includes integrated Radeon graphics — is not a barrier. Once a discrete GPU is installed, the system will automatically disable the iGPU for compute tasks, allowing full utilization of the dedicated card. The AM4 platform’s PCIe 3.0 bus will not significantly bottleneck either GPU for AI workloads, as model inference is more dependent on VRAM bandwidth and compute cores than PCIe throughput.

For those unfamiliar with AI tools, installing ComfyUI via Python and pip on Windows or Linux is straightforward, but requires the NVIDIA driver and PyTorch with CUDA support. The RX 7600, despite its newer RDNA 3 architecture, is not officially supported by most Stable Diffusion frontends. Even with experimental ROCm builds, users report inconsistent performance, driver conflicts, and limited documentation. In contrast, the RTX 2070 Super benefits from years of community-tested workflows, pre-built models, and troubleshooting guides.

Ultimately, while the RX 7600 represents a more modern and power-efficient design, the RTX 2070 Super’s software ecosystem makes it the pragmatic, low-friction choice for AI experimentation. For users serious about exploring AI art, investing in a GPU with at least 12GB VRAM (such as an RTX 3060 or 4060) is recommended for future-proofing. However, for a temporary test, the RTX 2070 Super is not just viable — it’s the clear winner.

For further guidance, users are encouraged to consult the official ComfyUI GitHub repository and the r/StableDiffusion wiki, which offer detailed setup instructions and model compatibility charts. As AI tools evolve, hardware support remains uneven — but for now, NVIDIA’s dominance in the AI space is still unchallenged in consumer applications.