[selfhoster11]

It does not have to be VRAM, it could be system RAM, or weights streamed from SSD storage. Reportedly, the latter method achieves around 1 token per second on computers with 64 GB of system RAM.

R1 (and K2) is MoE, whereas Llama 3 is a dense model family. MoE actually makes these models practical to run on cheaper hardware. DeepSeek R1 is more comfortable for me than Llama 3 70B for exactly that reason - if it spills out of the GPU, you take a large performance hit.

If you need to spill into CPU inference, you really want to be multiplying a different set of 32B weights for every token compared to the same 70B (or more) instead, simply because the computation takes so long.

[refulgentis]

The amount of people who will be using it at 1 token/sec because there's no better option, and have 64 GB of RAM, is vanishingly small.

IMHO it sets the local LLM community back when we lean on extreme quantization & streaming weights from disk to say something is possible*, because when people try it out, it turns out it's an awful experience.

* the implication being, anything is possible in that scenario

[selfhoster11]

Good. Vanishingly small is still more than zero. Over time, running such models will become easier too, as people slowly upgrade to better hardware. It's not like there aren't options for the compute-constrained either. There are lots of Chinese models in the 3-32B range, and Gemma 3 is particularly good too.

I will also point out that having three API-based providers deploying an impractically-large open-weights model beats the pants of having just one. Back in the day, this was called second-sourcing IIRC. With proprietary models, you're at the mercy of one corporation and their Kafkaesque ToS enforcement.

[refulgentis]

You said "Good." then wrote a nice stirring bit about how having a bad experience with a 1T model will force people to try 4B/32B models.

That seems separate from the post it was replying to, about 1T param models.

If it is intended to be a reply, it hand waves about how having a bad experience with it will teach them to buy more expensive hardware.

Is that "Good."?

The post points out that if people are taught they need an expensive computer to get 1 token/second, much less try it and find out it's a horrible experience (let's talk about prefill), it will turn them off against local LLMs unnecessarily.

Is that "Good."?

[jimjimwii]

Had you posted this comment in the early 90s about linux instead of local models, it would have made about the same amount of sense but aged just as poorly as this comment will.

I'll remain here happily using 2.something tokens / second model.

[apitman]

But local aka desktop Linux is still an awful experience for most people. I use Arch btw

[selfhoster11]

I'd rather use Arch over a genuine VT100 than touch Windows 11, so the analogy remains valid - at least you have a choice at all, even if you are in a niche of a niche.

[homarp]

agentic loop can run all night long. It's just a different way to work: prepare your prompt queue, set it up, check result in the morning, adjust. 'local vibe' in 10h instead of 10mn is still better than 10 days of manual side coding.

[hereme888]

Right on! Especially if its coding abilities are better than Claude 4 Opus. I spent thousands on my PC in anticipation of this rather than to play fancy video games.

Now, where's that spare SSD...