Choosing the right graphics card is crucial if you’re exploring the latest generative AI models like Flux, HiDream, and Framepack. But can you really get great AI performance on a £250 budget? And how does a dedicated GPU compare to Apple’s latest M4 Mac Mini?
GPU essentials for generative AI
Running advanced text-to-image and video generation models requires a GPU that excels in video memory (VRAM), compute throughput, and robust software support. Large VRAM helps handle big models and produce high-resolution outputs without memory bottlenecks. More CUDA cores and AI accelerators increase rendering speed, while solid driver support ensures better optimisation.
Top GPUs (£200–£250)
The NVIDIA GeForce RTX 3060 (12GB) stands out as the best GPU option in this price range. It offers a balanced combination of VRAM, speed, and compatibility with AI frameworks. While not the fastest GPU on the market, the RTX 3060’s 12GB VRAM provides ample capacity for larger models and higher resolutions, delivering around 6.4 images per minute at 768×768 resolution (Stable Diffusion benchmarks).
Another contender, the NVIDIA GeForce RTX 4060 (8GB), provides slightly faster rendering speeds, achieving about 7 images per minute. However, its limited VRAM could restrict the use of larger models and higher resolutions. AMD’s Radeon RX 6700 XT (12GB) offers competitive gaming performance and decent VRAM capacity, yet its lower AI-specific speed and compatibility quirks make it less ideal. Similarly, Intel’s Arc A770 boasts an impressive 16GB VRAM at a competitive price but suffers from immature software support, resulting in slower practical speeds of around 4.7 images per minute.
Verdict: Best GPU under £250
The NVIDIA RTX 3060 12GB is the clear winner for generative AI tasks in this budget range. Its combination of sufficient VRAM, solid rendering speeds, and excellent compatibility with popular AI frameworks makes it the most balanced and versatile choice for experimenting with image and video generation.
Running local LLMs (LLaMA, Mistral)
The RTX 3060 also comfortably supports local large language models (LLMs) up to 13 billion parameters using quantisation. For 7–8 billion parameter models, the GPU provides a fast and responsive experience (20–30 tokens per second). Models of around 13 billion parameters are also feasible, albeit at slower speeds (~7 tokens per second), provided appropriate optimisations are applied. This significantly accelerates local AI tasks compared to CPU-only setups.
GPU vs Apple M4 Mac Mini: Which is better for generative AI?
Apple’s new M4 Mac Mini offers unified memory, which allows it to handle very large models beyond typical GPU limits. However, it falls short in raw speed compared to dedicated GPUs. While the M4 Mac Mini might take around 20–30 seconds per image, an RTX 3060 GPU can produce the same image in about 10 seconds. Apple’s solution excels in scenarios involving extremely large models due to its unified memory, whereas NVIDIA GPUs are faster and provide superior software support.
Final thoughts: PC GPU or Apple Mac Mini?
For users prioritising speed, flexibility, and extensive software compatibility with the latest generative AI tools, the RTX 3060 GPU on a Windows PC is the recommended choice. Meanwhile, the Apple M4 Mac Mini is appealing for those embedded in Apple’s ecosystem who prefer a compact, energy-efficient setup and require occasional use of exceptionally large models.
In summary, dedicated GPUs deliver superior performance and ease of use for regular generative AI tasks, whereas the Mac Mini offers memory versatility and convenience in a compact package. Both solutions make generative AI technology accessible even on modest budgets.
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