Comment by mft_
10 hours ago
You're right, and it's interesting to consider why. It's probably a combination of a few factors:
1) Local LLMs are a relatively new phenomenon and hardware takes years. Apple probably lucked into their unified memory architecture being suitable (in terms of memory size and bandwidth) for local LLMs, but it's only with the newest generations we're hearing about LLMs even being a consideration in their design process.
2) NVidia seem to be deliberately blocking consumers from taking this path - as evidenced by the removal of NVLink from the 30x0 series onwards - probably to protect their data center cards from internal competition?
3) Perhaps there's just not the market for it? It's feasible that the number of nerds interested local LLMs is very small in numbers, sales, and profit potential compared to gamers on the one side, and data centers on the other. (This would explain why AMD and Intel aren't trying to out-innovate NVidia in this area, despite it being an obvious opportunity.)
There will be a huge market for local inference once it's cheap and widely available.
Try to imagine output token speeds of 15,000 tok/s and a time-to-first-token of 200ms. (This has already been done for Llama 8B.)
Now imagine gargantuan context windows (2M, 4M, or even bigger); keep in mind the 1M context windows were science fiction a few years ago... now imagine having this on a local model on something like a phone or portable device that can be gathering data about things you're doing and constantly run inference for things useful to you. An obvious example of this would be a chatbot you can talk to that responds like a normal human conversation and doesn't have delays, but that's just scratching the surface.
> There will be a huge market for local inference once it's cheap and widely available.
I've seen public pronouncements that the RAM shortage could persist for a decade.
And then if consider that the constraint on local LLMs isn't just memory size but bandwidth ...
If you take something like a DGX Spark and increase its memory to 512GB that doesn't even solve the problem. Because the bandwidth of DDR5 just can't manage reasonable speeds for decode. If you take a dense model or an MoE model uses up most of that 128GB in active decode you will only get like 15 tok/sec. "Real" datacentre inference boxes use high bandwidth memory that is 10x the speed.
I think we're unfortunately a long way off, unless people learn to accept working with much less intelligent models locally.
The innovation is going to have to come on the research & software side -- we need to find ways to pack more intelligence into a smaller number of parameters.