It's absolutely not for these models. There are plenty of consumer GPUs out there with 8 or 12GB VRAM - they are comparatively very fast at inference but just aren't big enough to run lots of the models you want. Also context management is a massive pain.
for inference the compute is the last thing we need more of.
memory bandwidth is the numebr one blocker, after that the inefficiencies that where introduced with MoE models (and all new large models are made that way)
It's absolutely not for these models. There are plenty of consumer GPUs out there with 8 or 12GB VRAM - they are comparatively very fast at inference but just aren't big enough to run lots of the models you want. Also context management is a massive pain.
I run qwen3.5-9B on an RTX 3080 with 10GB of vram. It runs at ~77tk/s with around 50k context size.
As soon as I switch to a model that doesn't fully fit into vram it tanks to <10tk/s which makes it unusable for me for most tasks.
RAM bandwidth is the main issue for running LLMs on consumer hardware...
RAM is not “never” the issue. My iPhone and MacBook Air could both run larger and more capable models if they had more RAM.
Quite the opposite, RAM is always the issue. More specifically, high bandwidth RAM.
what??? not true!
for inference the compute is the last thing we need more of.
memory bandwidth is the numebr one blocker, after that the inefficiencies that where introduced with MoE models (and all new large models are made that way)
Here is a quick read: https://news.ycombinator.com/item?id=49324600
and memory bandwidth