If it is a mixture of experts (MoE) model like the 2.x models, won't this reduce the hardware needed to run the model?

The Kimi-K2.6 model is 1.1T parameters with 32B active parameters. With light quantization (Q6_K) that's enough to run it (slowly) on a single 5090. On a single B200 you can have 5-6 experts loaded into VRAM at a time. Realistically that would be 3-4 to account for the context. [!]

[!] With this and other MoE models it looks like an interesting area for research would be to detect or predict which models would be needed ahead of time. That way you could schedule the load into VRAM step before the weights are needed. That way you shouldn't lose much/any performance from offloading the weights to RAM.

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petu 5 hours ago | parent | next [-]

You need whole weights in VRAM for optimal performance. Don't be confused by "experts" in the name -- you don't get to load static subset of experts and blast next 100 tokens with them. In typical MoE model they get switched "randomly" on every token, so all experts have to be readily available.

> With light quantization (Q6_K) that's enough to run it (slowly) on a single 5090. Kimi K2.6 is released as INT4 already.

So 5090 with K2.6 is just gonna sit idle 99% of the time, waiting for next slice of weights to load.

5.6 Sol calculates that single 5090 in raw compute & memory bandwidth can run K2.6 at 35 t/s (256k context depth) -- if it somehow had enough memory to hold whole model in VRAM. Man, I hope HBF succeeds and Nvidia brings it to consumer cards in 5 years..

	▲	zozbot234 5 hours ago \| parent [-]
		> In typical MoE model they get switched "randomly" on every token, so all experts have to be readily available. It's worse than that: a typical MoE model routes a separate set of experts at every layer, not just every token! But in practice, RAM offload (for systems with non-unified VRAM) and even SSD offload still work surprisingly well given some amount of caching. You can likely recover compute intensity and throughput by batching requests together, which (in practice, depending on sparsity) will end up reusing some of the loaded experts with high probability; though the obvious tradeoff is that having to store KV caches for the wider batches may leave you with less room to cache experts across layers and tokens. (Plus if you're batching so widely that you end up loading essentially entire model layers, MTP then becomes applicable even for a MoE model. But this typically only applies if you're doing inference on a very large scale, or if your memory bandwidth is so scarce that you have to recover compute intensity by any means feasible.)

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embedding-shape 6 hours ago | parent | prev | next [-]

> The Kimi-K2.6 model is 1.1T parameters with 32B active parameters. With light quantization (Q6_K) that's enough to run it (slowly) on a single 5090

Without leveraging system RAM and/or SSDs, I don't think you can, or how exactly are you running this, if this is something you are doing today? With CPU/expert offloading you could probably do it with a 5090 + 1TB of RAM or something like that, but absolutely not on a single 5090 entirely within VRAM.

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robotswantdata 6 hours ago | parent | next [-]

Yes hybrid approaches are much better than people realise.

There are a lot of optimisations that are not in the public sphere, source working on start up in this space

	▲	embedding-shape 5 hours ago \| parent [-]
		> There are a lot of optimisations that are not in the public sphere Sure, but if we're participating in public discussions, isn't it more fun if we talk about things people can actually read and understand, rather than secret stuff other's can say work, but no can actually validate or know how it works? It sounds like "hybrid approaches are much better than the public is aware, because everything else is private and secret", but also: ok, so what? No one can run that anyways, (yet?), so why it matters?

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rhdunn 6 hours ago | parent | prev [-]

Yes, that's what I was saying w.r.t. expert offloading, i.e. ensuring that the GPU could fit the active parameters not all the parameters.

	▲	embedding-shape 5 hours ago \| parent [-]
		Alright, I guess I misunderstood. To be fair, this part: > The Kimi-K2.6 model is 1.1T parameters with 32B active parameters. With light quantization (Q6_K) that's enough to run it (slowly) on a single 5090. Is painting a very different perspective, even considering the latter parts it's hard to read that as "Of course offloading everything else that doesn't fit on the GPU itself". But anyways, it's been clarified now so no harm :)

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walrus01 6 hours ago | parent | prev | next [-]

I assume you mean putting only the 32B active parameters on the GPU, and the rest on a bunch of regular server DRAM like on a 768GB to 1024GB RAM server?

Because Kimi K2.6 in Q4 is about 584GB GGUF size on disk and will use slightly more than that in RAM, Q8 is 595GB.

https://huggingface.co/unsloth/Kimi-K2.6-GGUF

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NitpickLawyer 6 hours ago | parent | prev [-]

You're talking about running this "at home" for 1 user, using a mix of VRAM and RAM (total should be ~1.5TB). That's certainly possible. It'll be slow, especially prompt processing, but doable for single users.

But my comment on running it was more towards serving this profitably at scale. You get much better throughput / unit of compute if you load everything in VRAM and serve many requests at the same time. That's how all inference providers are doing it.

	▲	rhdunn 6 hours ago \| parent [-]
		I was talking about running this on a server, hence my comments re 1xB200. Obviously, the more hardware/VRAM you have the better/faster you can run these large models. But if you are a small/medium sized company you could feasibly do it on just one B200. It all depends on how much hardware you can afford to run.