Huge price difference in what you can do with buying a used 4U rackmount server and putting 3TB of RAM in it (64GB DIMMs x quantity 32 in a quad socket xeon, you can see some benchmark prices on eBay for sets of 16 or 32 matched 64GB ECC DIMMs) for <$30,000, vs the cost of trying to run it on real GPU hardware.
Now obviously, as of the time I write this, the full precision hasn't been released nor has anyone like unsloth run it through quantization yet to produce a "Q8" or "Q8-XL" variant of it. But I think it's going to need more than 1536GB of RAM, with a usable and large amount of context, more like 2TB and preferably 2.5 to 3TB.
I also predict that people who try to run it in Q4 and Q6 will get the worst of both worlds, less precision/lost knowledge but also not reliable output that comes out too slow. In my personal opinion if I'm going to deal with something that is smart but slow and running on limited budget hardware, I need it to be Q8.
You'll spend ~100x more on electricity than the API cost to have it run on someone else's GPU at several hundred tokens per second.
I think some sort of extreme data privacy requirement is the only situation that justifies this, but the intersection of {needs absolute data privacy, needs to run SOTA model, cannot afford GPUs} is really really narrow. I wouldn't be surprised if this is an empty set.
There are parts of states like Grant County Washington that have cheap hydro power, but it's very rare for power to be that cheap in the US. Even if this applies to you, it won't apply to the vast majority of people on here who will have electric rates 2-4x higher.
Average electric rates by region:
New England 28.1 cents
Mid Atlantic 25.1 cents
East North Central 20.8 cents
West North Central 14.8 cents
South Atlantic 16.1 cents
East South Central 15.5 cents
Mountain 14.6 cents
Pacific Contiguous 26.1 cents
Pacific Noncontiguous 42.1 cents
I think you can get down to around 8 if you are signed up for an interruptible load, or a dedicated off peak load, depending on the company, but yeah, standard rates aren't that low.
This is a bit misleading, because it's combining the 50 cents/kWh from California with 15ish cents/kWh in Oregon and Washington. Seattle City Light, for example, charges 13.38 cents/kWh on flat rate pricing, and far less with time-of-use billing (8 cents/kWh on off-peak).
From my last bill
> KWH USAGE 2590 - $183.37
There's a base customer cost of $18 on top of that, but yeah ~$0.077/kWh taxes included.
Last one was $212 for 2,146 kwh between June 8 - July 6 (28 Days)
Using myself as an example:
I adjust my A/C to run outside of 5pm-9pm (peak) if at all possible, we try to avoid pointless high-draw usage during that same window, and both of our EVs hold off charging until after 9pm.
My rate from 5pm-9pm is 0.43/kWh. My rate after 9pm is 0.09/kWh. The flat rate alternative, if I did not want to worry about time of day, would be 0.21/kWh. These prices are all-in, including transmission and distribution/whatever.
It would be dishonest to say that my EVs only cost me 0.09/kWh to operate, which on it's face is a claim to paying over 50% less. In reality, time of day pricing typically saves me somewhere between 10% and 15% in an average month compared with flat rate.
You can do the same thought experiment with say a dehumidifier in your basement. It can easily be off during peak usage and still accomplish its job, so its cost of electricity is also the marginal off-peak rate.
It would cost me more (modestly so, less than 10%) to be on TOD without the EVs. This will vary by customer, of course, and I expect that the power company designs TOD to be a wash for the average customer. They even guarantee it won't be more than 10% more expensive over the first year or they will refund the difference.
I guess it depends on if you would be using ToU otherwise.
It looks like about 50% of Californians use ToU plans, but the number is only 10% nation-wide.
https://www.hydroquebec.com/residential/customer-space/rates...
Another example would be Manitoba hydro
All figures in Canadian currency
Around here electricity companies quote prices like yours but that is supply only while transmission, taxes, and fees are again as much on top. Is that really all inclusive?
People should always compromise speed for data sovereignty! Who said: that in this digital day and age, information about money is more important than money!
1. Their API server provide an attestation JWT. This JWT is signed by Google's private key. 2. The attestation has details on the running container. I suppose the container host is a Google-provided distro and Google's signer will verify that the OS is theirs and up-to-date. 3. They could've proxy the attestation. To prove this is not the case, the field eat_nonce include the TLS certificate fingerprint, which should match the API server you're connecting to. I suppose you will need to pull their container and verify from the source that the container itself generate the private key, it never leaves the container, and the container has no way to run arbitrary code such as SSH or vulnerabilities.
Do you actually need to run the state of art model at 5 tokens per second instead of a qwen or whatever 7b or 30b model at 100 tokens per second?
On the other hand, would it be cool to also have a really big thing as an ancillary tool that I could throw a request into opencode before going to bed, let it crank away and take a look at what it's done 7 hours later? Yeah, particularly if I (very much an unknown quantity at this time) could be confident that it builds high quality, syntax valid, appropriately commented and not absurd code.
Some people like doing things they want to do. Do I actually need to buy expensive pigments from europe to make paintings of flowers? My camera produces a much more accurate representation.
a) Contracting with a third-party independent inference provider who will run your choice of model on fast hardware that they own, with all appropriate data security/privacy/contractual/compliance protection in place
or
b) Contracting with the original creators of the model to run inference via their API and with assurances that all the same data protection is in place
or
c) Spending the money to buy your own inference hardware to run it on something you fully own/control at proper usable speeds?
Edit: Everything I've been writing in this thread is mostly within the context of being able to evaluate K3 and its usefulness to be self-hosted as a preliminary proof of concept or test of feasibility of a new thing, such as on <$20,000 of server hardware, before proceeding to spend 300-400k on GPU-related hardware, or external third party services/ongoing billing.
They'll give you HIPAA compliance, they even have a data center for US government classified data, they can give you European data sovereignty. And with OpenAI and Anthropic models to boot, you don't even have to settle for open weights.
What kind of privacy needs do you really have beyond that?
Even for EU companies running open weights on EU stacks LLM inference on the GPU must process plaintext and I can't find any EU provider with NVIDIA H100/H200/Blackwell CC mode plus SEV-SNP or TDX, where you can cryptographically verify the workload ran somewhere the operator cannot inspect.
Personal compute is therefore the only option if you want personal autonomy privacy for IP &c. Maybe another option is to use cloud compute rented to fine tune a personal model that suits your own needs that would help bring the cost down, I don't know enough about this area to know if it kills the "intelligence" of those domains due to limited ?cross-verification within the LLM.
for anyone not US-based, this company is hostile and you have to assume the US government can and will force them to give access to your data.
A trillion-dollar business can easily trade dollars for the privacy. A business with $1M to spend won't even get a phone call with OpenAI or Anthropic, who were the only* previous players in town for doing this.
Worst-case example: Bootstrapped startup working in military.
It's also the case that an open model enables many more intermediate-cost solutions. E.g. providers certified for specific applications, on-prem rentals, etc.
* Omitting Azure, which gives some privacy for some $$$ on their models, but not at the level of high-security.
That's the easiest case.
AWS Bedrock models running in AWS Secret Cloud for Industry. (I really have no affiliation with them, I'm just like... this is a completely solved problem, why do people think this is hard and requires on-prem hardware?)
https://www.aboutamazon.com/news/aws/aws-secret-cloud-for-in...
I'm with GP that these are tinfoil hat concerns, when there are solutions to all of these, unless you're perhaps in some country with very specific needs beyond things like European sovereignty or US military secrets (like a non-US defense concern).
Note that the other commenter never said US-based military oriented startup. You just assumed, then jumped to "heck yeah let's use Amazon Secret Cloud for Industry"
Not everyone has or wants an office in Crystal City.
If I were ranking third parties on their ability to safely handle my data without compromising it, I would rank Anthropic pretty low for things like Fable (where they more or less promise that they will misuse my data), but I want Azure pretty low in the sense that I fully expect them to be compromised.
I would tend to trust Amazon to avoid being compromised.
The provider needs to comply with specific rules, have specific certifications, and sign specific agreements. You check the boxes, and you're good to go.
Microsoft does that better than anyone. OpenAI and Anthropic don't do that at all. Google does that rarely and poorly. AWS is not bad, but not as good as Microsoft.
Azure was always my go-to for regulated applications in the cloud. Some do require e.g. on-prem or even air gap, where even Azure is out.
I have some inference I simply don't want to run on OAI, Anthropic, or Google because I don't want to run afoul of their "rules" and end up with a banned account, and this situation is only getting worse when it comes to doing fairly basic tasks like trying to secure your app against security problems.
Qwen 3.6 is another matter. Paying provider rates for the amount I run locally would put me in the thousands of dollars. So that's very practical to buy a Macbook instead, plus an RTX card, and so on.
Are there? At the highest levels of defense and law, AWS and Azure are used.
Having tried selling some of these entities on doing things in-house, there seems to be little interest.
This is certainly true if the user is an American company. You could look at the European initiatives to run this stuff on hardware they own in facilities they own and control within the borders of Europe for a counter-example.
Such as: https://www.google.com/search?client=firefox-b-d&q=schwarz+s...
https://www.dutchnews.nl/2026/04/government-turns-to-german-...
Hopefully that changes!
I don’t know if that’s 100x more than I’d pay (opex-wise) with an nvidia setup, but I can say the one-time capex is a great deal cheaper. Avoiding VRAM and DDR5 (fast DDR4 should be OK) are the biggest cost savers. ECC RAM is worth the extra price. General datacenter-quality hardware has less price sensitivity, and plenty of bang for your buck.
Under heavy inference load you will find that the cpu usage is actually less as the bottleneck is the RAM bus speed. An older 2U rack server that is 600W load (typically a 1+1 power supply server when plugged into two kill-a-watt would show 300W on each, equal load balancing) when maxed out with stress-ng might be only 450W total running inference.
If you have 600kWh used in a month by running something 24x7 and your power is $0.15 a kWh, that's more like $90/mo (not counting cooling or any ancillary costs for the environment where it's in).
The options for AC power supplies for servers with 2 or 4 load sharing redundant power supplies are a lot greater. If you were buying all new hardware and starting from a clean sheet of paper design with lots of money to spend, absolutely. At that point also start looking at higher voltage DC distribution stuff related to open compute platform and 800VDC.
But if I were trying to make the absolute most use of $20,000 to put together a 3TB RAM server (48 x 64GB DIMMs), it would likely end up AC powered.
Where in the world are you finding that much RAM in a racked server for $200/month?
That's a world that I don't think we're ready for.
Young men 14-?? already compromise and attempt to extort organizations daily, sometimes cluelessly from western nations, often not. It doesn’t have to be gangs when the home country doesn’t care / isn’t technologically or culturally developed.
Already seeing AI-written payloads and frameworks in the wild. I think it’ll turn out that AI won’t build you a maintainable ERP but it can create C2 networks, exploit POCs or even 0-days potentially, and let kids make their own ransomware tooling. Then we’re dealing not with a handful of cybercrime tool makers but a generational problem.
Don't get me wrong, slow is sometimes better than "not at all", but depending on the performance, it might end up way too slow to even work for batched/async jobs like that.
And then that's just for single prompts, what about agent harnesses, where before every tool call the model could reason a bunch?
I agree with you that real world results would be interesting, but I wouldn't hold my breath nor expect it to realistically be able to be useful. Still, people should try it, for science if nothing else :)
So full CPU local AI inference may become viable option in coming years.
Or from SSD using something like Colibri[1]. Not going to be quick, but at least runable.
edit: the results I have seen from people trying colibri with fast consumer grade PCI-E 4.0 NVME SSD are 0.1 tok/s on models that are <700B in size, things that are well under 800GB on disk. With something that's 3T in size it'll probably be a lot slower than hat.
But still fun you can run it at home.
The model is known to be MXFP4 according to Kimi's release blog post, so the model weights will be less than 1536GB: https://www.kimi.com/blog/kimi-k3
Also, their previous models were native INT4, so it would be weird if they went larger now.
* Sparse Experts: 1481.4 GB
* Dense Experts: 1.9 GB
* Self-Attention: 72.4 GB
* LLM Head: 2.4 GB
* Embeddings: 2.4 GB
* Vision Encoder: 0.35 GB (surprisingly small)
plus some miscellaneous parameters.
Most importantly, we now know that the model has 104B active parameters, which is quite a lot and will make it difficult to self-host efficiently.
On a 3T model I’d imagine you’d be closer to 0.05 tks
If wonder if you can train a model to optimize this, by trying to make the expert selection sticky across a few tokens, without too much quality loss.
Another fun idea might be to try to build a model where the router chooses the expert 1-3 tokens in advance.
You can!
> AFM 3 Core Advanced makes routing decisions per prompt. A lightweight, dense block selects a fixed set of experts during initial processing, periodically reselecting them during generation.
https://machinelearning.apple.com/research/introducing-third...
But the memory bus speed is fully committed when generating tokens or thinking.
I've made some proof of concept in https://github.com/woct0rdho/transformers5-qwen3.5-recipe . We can finetune Qwen3.5-35B-A3B in 16 GiB VRAM, and DeepSeek-V4-Flash (284B-A13B) in 90 GiB VRAM, without CPU offload. This works well on unified memory machines like Strix Halo.
Even so, larger models like Kimi-K3 still require multiple GPUs and nodes, and there are a lot more to do compare to single-GPU training.
GGUF is maintained by all the llama.cpp developers. There are many quantization formats and algorithms under this container format, some are optimized for MoE (such as APEX quant), some for CPU and some for GPU, some work surprisingly well below 4-bit (and even near 1-bit). It also supports recent architectures like linear attentions and mHC.
It's almost certainly full parameter post training of the original model weights.
No, you don't. Without training cost you can infer only the marginal cost of serving this kind of models.
Moreover, you don't know the actual size of closed models (what if Fable is a 10T model? What if it's 1T?)
Still useful; "are the labs marginally profitable just on the marginal inference costs?" is still a useful question to answer. After all, if they aren't even profitable on inference in isolation, then we can expect to see large price increases.
If they are able to turn a marginal profit on inference alone, then perhaps the price increases won't be so severe (or perhaps they expand the time between generations so that they spend less on training but take longer to complete training).
"Are the labs profitable at all?" is, of course, a much more useful question, but that doesn't mean that the first question is completely useless.
The K3 maths can turn true only if the models size is roughly the same and the labs didn't find any better way to run inference at scale.
We don't really know that, for OpenAI and Anthropic. We suspect that, but as far as I know, even they have stopped claiming that they are profitable on inference.
So which is it?
Anthropic was probably profitable last quarter, without training costs: https://www.wsj.com/tech/ai/mind-blowing-growth-is-about-to-...
Which is by far the most interesting number of the two.
> Moreover, you don't know the actual size of closed models (what if Fable is a 10T model? What if it's 1T?)
If you get close in output quality, then does that matter?
When you're trying to estimate/infer the costs of serving the tokens and even include the cost of training the weights in order to output tokens then yeah, why wouldn't that matter?
Only if you don't have to continuously train new models, and you are not at a runway risk.
Of course inference efficiency is dictated by model architecture, size, etc. You can still guesstimate some of those and have an idea about cost/serve at several size tiers.
I guess this is one of the reason Anthropic i so "active" for asking for a development break.
Are you talking about Kimi's training cost or the training cost of the model(s) that Kimi distilled?
Because Moonshot didn't even incur the majority of the training costs either
The distillation process involves getting conversation traces from the model you are distilling from, and then training your model against them.
You still have to do the training!
I other words, the providers that will be offering K3 inference don't have any training costs to offset, so they are only charging for the inference itself. OAI/Anthropic would need to offset their R&D and training costs in order to not be selling API access at a loss.
If you're going to open source your model, why would you set your own price high enough that other providers could easily and profitably undercut you?
well, exactly.
that's tough to answer without just average sampling because some users will ask the model "what's todays date" or "what color is the sky?" and some users will ask "Let's rewrite the linux kernel in brainfuck."
I think this release is actually great both ways when you think about it. We gonna be able to learn knowledge that labs have been hiding from us (e.g. cost like you mentioned). And labs could learn from whatever optimization techniques people come up with when trying to host this model.
It's honestly just good for everyone in my opinion.
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.
> 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..
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.)
Caching really has nothing to do with this. With RAM offload you can mostly benefit from:
1) Batching for prefill is a huge win, even with MoE, since the batch sizes can be so large.
2) Keeping non-expert weights in VRAM, so the percentage of weights used per token in VRAM is higher. This benefit reduces with larger models, though.
> 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;
With MoE it's low probability.
> MTP then becomes applicable even for a MoE model
With MTP it becomes _extremely_ low probability.
For even the sparsest MoE open models, having more than a handful of inferences in the batch is enough to make it more likely than not that you'll get some MoE weight reuse within any given layer. This assumes totally random sampling, ignoring any cross-request correlation that would push that probability even higher in many practical scenarios.
> With MTP it becomes _extremely_ low probability.
This is actually right, MTP is only ever worthwhile in very special cases involving either dense models or extremely wide batching of MoE ones that somehow still leaves unused room for parallelization (which AIUI would involve an assumption of very abundant compute with very limited memory bandwidth).
If you tell me the model and the number of parallel streams, I will do the math.
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.
There are a lot of optimisations that are not in the public sphere, source working on start up in this space
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?
> 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 :)
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.
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.
Take a look at GLM 5 vs GLM 5.2 pricing -- GLM 5.2 cost more despite being the same model.
Take a look a look at DeepSeek, which hosts DS v4, profitably, yet others aren't able or willing to match the price.
OTOH, the multiple providers who have settled around the same price point ($3.48/M output tokens for multiple providers with good reputations) does indicate where it is profitable: https://openrouter.ai/deepseek/deepseek-v4-pro#providers
But I do think that the median price where this settles will tell us something about the floor at which it is profitable to serve this model.
> DeepSeek, which hosts DS v4, profitably
I specifically mentioned 3rd party providers, because there can be an argument that model creators themselves are subsidising tokens to gather training data for the next model. In fact, ds are public about their gathering of data (at least on openrouter they're marked as such). So that 0.x price point for dsv4-pro is likely subsidised.
For my product, I run GLM 5.2 and other models myself, in production, on rented hardware. Paying API prices would cost much more.
EDIT: You can now see several other third-party providers for Kimi K3 (Nebius, Fireworks). All charge exactly the same as the first-party. Does that mean that their costs are the same? Seems quite unlikely. It's simply not an efficient market, yet.
they noted in their blog post they didn't focus purely on coding for grok 4.5.
> reduces it down to its minimum entropy -- it cannot be compressed further.
I think you could make a lot more money elsewhere :-)
https://en.wikipedia.org/wiki/Kolmogorov_complexity#Formal_p...
It depends on the total entropy of the model. Smaller models have less entropy.
Interesting. Why is that? I would have expected the opposite, since larger models have to try less hard to fit the training data. Or maybe this leaves more parameters with random initialization, resulting in higher entropy for larger models?
LOL
I am curios what's the most profitable thing to "plant" (agriculture analogy) on the land (cards) that you have have: web hosting, vps, llms, image/video generation, etc
Sounds like I'm buying a lottery ticket this week so I can drop $800k on hardware.
> SemiAnalysis estimates that Anthropic's current blended gross margin has risen to the mid-60% range, with the API business gross margin exceeding 80%
Of course, people will insist "they are lying", "why should we believe them, it's well known they subsidize API pricing", ...
https://newsletter.semianalysis.com/p/anthropic-3q26-profit-...
https://finance.biggo.com/news/02d45650-b569-4d12-b44d-8d6d8...
But if they're hoping to recoup non-recurring engineering costs rather than just hardware costs, they do need to consider the useful lifetime of the specific model.
Sota closed models don't even answer cybersecurity questions lol.
my friends whove tried in their companies gave up on it.
Everyone keeps thinking those A100s only have 6 more months of life, and yet they're still going for more than they did per hour in 2024.
Show me evidence that A100 prices have collapsed, and maybe GPU depreciation will be relevant to the market.