Honestly, 1500 bucks is a solid budget if you play your cards right. I have been running local LLMs for years and honestly, the VRAM debate is the only one that matters. If you go the PC route, snagging a used NVIDIA RTX 3090 24GB GDDR6X is the move. I did this last year and it changed everything. The 24GB of VRAM allows you to load significantly larger quantized models which is way better than struggling with small ones on lower memory cards. Yes, it runs hot and draws a ton of power, but for coding tasks, it is basically a beast. Here is what I found over time:

• VRAM is the primary bottleneck, dont skimp on it.
• If you choose a Mac, you need to ensure you have enough unified memory because the GPU uses that same pool.
• The Apple Mac Studio M2 Max 32GB Unified Memory is decent, but you might find 32GB of total RAM limiting pretty fast if you want to run larger parameter counts. Building a rig around the 3090 gives you upgrade paths later, like throwing in a second card once you have more cash. The Mac is super quiet and sleek, but you are locked into the specs you buy on day one. If you want pure performance per dollar for deep learning, go the GPU route. Just get a case with good airflow and you will be fine.

> My current setup runs quiet and cool, but it took some trial and error @Reply #2 - good point! Honestly, the heat from a 3090 is no joke if your ventilation is bad. Since you have 1500 bucks, you might want to consider building a system around a pair of NVIDIA RTX 3060 12GB GDDR6 cards instead. You can find these used for cheap and together you get 24GB of VRAM without the insane power draw or thermal throttling of the high-end flagships. Be careful with the motherboard selection though. Make sure to get a board with enough PCIe spacing so the cards arent choking each other. It saves a ton of money for a better Corsair RM850x 850W Gold PSU which will keep things stable. Just keep an eye on your local listings for deals, dont rush into buying new.

Late to the party but I actually went through this exact headache last year when I started tinkering with local models. The heat from those high-end GPUs is no joke, especially if your office is small. My current setup runs quiet and cool, but it took some trial and error to get the quantization right so things dont just crawl.

• Prioritize memory bandwidth over pure compute speed
• Look into cooling solutions if you stick with a tower
• Dont ignore the power supply overhead I personally learned that optimizing the model size matters way more than having the absolute latest silicon. Once I stopped stressing over benchmarks and just focused on getting the token speeds usable for coding, the whole process felt way less intimidating. Youll figure it out, just take it one step at a time.