The benefit is that you can use these models on a variety of different providers. You're not stuck with a single proprietary provider. Aggregators like OpenCode Zen and OpenCode Go can shop around providers on these.

I regularly use together.ai with models like this. I recently discovered abliterated models (models edited to not refuse to work on security stuff) and hosts for them like refuseless.com, and that's an option for open source models, not for proprietary ones.

Models this size aren't targeting hobbyists running models in their bedrooms on gaming/surplus hardware bought with disposable income.

This model is for researchers who already have racks of equipment, inference providers who sell access to models, and for businesses who must (or choose to) use on-prem models. All of which have to pay to use the model if their revenue rises beyond certain thresholds: https://huggingface.co/moonshotai/Kimi-K3.1-Pro/blob/main/LI...

You could easily run it with SSD streaming on as little as 192GB RAM (The dense portion is around 115GB or so at native precision, and the remainder would fit KV and a cache for the most commonly referenced experts.) This fits Gorgon Halo, which is a reasonably priced platform. It would be slow, but still usable for high-value tasks.

Possible data center bans incoming + tons of GPUs sitting in warehouses due to lack of power infrastructure to power them.

Perhaps a flood of 8x B300 will be homelab ready in a few months

AND developer cloud has an image for K3 running it on a single MI300x. Offloading to system ram can be quite decent.

I cant say how well that image works as I'm still waiting for them to approve my gpu access

Do you need 450k context?

I usually keep under 200k if I can help it for Claude/ open ai

And lower locally on my Mac to save on prefill.

If you use it day-to-day you can definitely feel that K3 (and K3.1) are much stronger than DS4.1-Flash-1017. There's something the benchmarks don't capture. DS4.1-Flash-1017 is an amazing model for the price, but it's not a very large model in comparison.

I wouldn't over-weight internal benchmarks from a competitor that claim benchmaxxing TBH. Kimi K3 has been at the top of Design Arena for open-weight models for a long long time, and I suspect K3.1 will be there for a long time too. It's pretty hard to fake that one since it's actual human preferences for the artifacts produced.

For reference, Hy4 Preview was a 770B model (49B active) + 10B MTP (0.7B active). Not nearly the same potential as Kimi K3 (which is 2.8T) wrt. real-world knowledge - which matters at the new frontier of advanced research and scholarly tasks. Coding benchmarks are pretty much universally saturated by now which is why newer intelligence indexes have chosen to weigh tasks quite differently.

Depends on what you're using it for. DSf is pretty good at executing planned coding tasks but it isn't very intelligent. K3.1 doesn't need to be a coding master if it can be a fable replacement.