Comment by freediddy
5 hours ago
I keep seeing more and more LLM models being loaded by incredibly under-powered machines. Is the GPU/memory crisis all lies? I get that running on an RTX 5090 will be much faster, but if we can use main memory instead of VRAM and get barely usable results, what is going on?
all the effort in this space is going into multiuser, datacenter workflows for high throughput inference. running in resource constrained environments is not where the money is. but it will be, especially if we look forward to a world where having 512gb unified ram is normal for "workstation" machines. The semiconductor space is slow enough to respond that it's likely it will take a few years before production capacity has ramped up enough to get us past the current supply crunch but it seems inevitable to me that we'll be able to run huge models like kimi 3 locally in the next few years (maybe 2029/2030 for it to really be affordable)
I think it depends on usage pattern. You trade speed for lower memory usage. Maybe engine specialisation and faster SSDs is the future for local inference, who knows
Or maybe it's just lazy programmers, wouldn't be the first time.