For those of us running Ollama on whatever hardware we already have: r/LowEndLocalAI

One of the things I like most about Ollama is how low it makes the barrier to experimenting with local models. The downside is that once you move away from high-end hardware, finding the right information becomes surprisingly difficult. A lot of us are asking very specific questions: Which model is actually worth running on 8 or 16 GB of VRAM? Is a larger model with CPU offload really more useful than a smaller one fully accelerated? How much context can I realistically use before performance falls apart? Does quantizing the KV cache make a meaningful difference on my particular machine? What works well on Apple Silicon, an AMD APU, an old GPU, or CPU-only? That is why I recently started r/LowEndLocalAI. The idea is not to create another general local-LLM subreddit. It is to make hardware constraints the default context. There is deliberately no definition like “under 16 GB VRAM”. A constraint might be VRAM, system memory, bandwidth, power, compatibility, old hardware, or simply not wanting to buy another GPU. What I would especially love to collect are Ollama setups where people include: hardware + RAM/VRAM + model + quant + context + relevant settings + actual performance + what they use it for. Because “it loads” and “I actually use this every day” are two very different benchmarks. If you are running Ollama on a laptop, Mac, mini PC, iGPU, CPU-only box, older GPU, or any other constrained setup, r/LowEndLocalAI is meant to be a place where those results are easy to find and compare. And if you have discovered some obscure setting that suddenly made your supposedly inadequate machine useful, please share it. Those are probably the most interesting posts of all. submitted by /u/soadsob [link] [comments]

Source: r/ollama | 2026-08-25