It will be very interesting to see what kind of 'slow' performance people get from running it on a no GPU, but tons of RAM server (like a dual or quad socket xeon with 1.5 to 3TB of RAM). For the purpose of giving it longer duration tasks to generate a piece of something and come back and check on what it has done in 4 or 6 hours. Even if the output is like 5-6 tok/s, that might be usable for some purposes.
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.
> Even if the output is like 5-6 tok/s, that might be usable for some purposes.
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 a number of use cases where sending the contents of your context and prompts (and the resulting output) to a 3rd party service is off the table as an option, and people will compromise speed for data sovereignty. And not everyone's electricity is equally expensive, I pay about $0.075 USD per kWh. It would for example cost me about $48 a month of electricity (not counting cost of cooling) to run a quad socket Dell R940 for a month.
That's an unusually low electric rate for the US - way below the lowest state average which is Idaho at 12.4 cents. It's certainly possible that you are getting 7.5 cents including delivery, but I've had friends say that they're "getting 13 cents per kWh" here in Massachusetts, but that's just the supply rate and the delivery is another ~18 cents.
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:
https://www.eia.gov/electricity/monthly/epm_table_grapher.ph...I pay a little less than that though I’m limited to 50 kW average load. Delivery is a fixed monthly fee, about $10 USD/month.
I'm actually getting 11 cents in winter, 13 in summer, but my utility company is a co-op. Average for my state is I think 19 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.
> Pacific Contiguous 26.1 cents
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).
Fort Collins, CO is probably where that rate is. I don't know of anywhere cheaper for residential electric.
Nashville Electric Service rates are 12.0 cents / kWh. Flat rate, no peak or off peak rates for residential.
If you run off solar with battery backup, you can achieve lower than those rates! Look at Time of Use rates. The super off peak rates instantly become the max price point once you pair TOU with Solar + battery.
I'm in Arkansas and get rates fairly similar as quoted.
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.
Is that for a month or a year?
My monthly bills are similar to this, near Arkansas.
Last one was $212 for 2,146 kwh between June 8 - July 6 (28 Days)
76kWh a day, does that include a lot of EV mileage?!
For me at least, yes but it's not an EV specific rate plan just our general one.
~80mi a day + AC is the vast majority of my use (for why so much AC - it's 102f [39c] and feels like 115f [46c] right now as I type this).
A lot of people quoting low rates are also just referring to their off-peak rate. This is pretty common in EV discussions. It's not exactly a fair argument there, either, because the flip side of having an off-peak rate is that the on-peak rate is usually quite a lot higher. So the true effective rate is a bit higher, somewhere in the middle depending on usage pattern.
It's easy to have your EV only charge off-peak, though. It's just a setting.
My point is that the tradeoff to get off-peak pricing is that on-peak is way, way more expensive. So you can charge the EV off-peak to maximize the savings, but everything else you do during on-peak time costs way more.
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.
If you leave the EV charging out of your consumption, does a time of day plan still save you money on the remaining usage? Or does it cost you? If it saves you money, then it would make sense to be on a ToD plan regardless of EV charging. Which means it makes sense to consider your additional EV draw as costing the marginal off-peak rate. Essentially the EV load has the valuable property of being dispatchable.
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.
> If you leave the EV charging out of your consumption, does a time of day plan still save you money on the remaining usage? Or does it cost you?
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.
Then yes I'd agree with you, it doesn't make sense to describe your EV charging as costing $0.09/kWh since you're presumably only on the slightly more expensive ToD plan due to the EV.
Personally I'd love to have a ToD plan, especially with the rate structure you've laid out - break even seems to be using less than 1/3 of your daily electricity usage during the on-peak hours, which is only 1/6 of the day? I've got a bunch of fixed loads (computers) plus ones that tend to run overnight anyway (dishwasher, dryer, etc).
FWIW I'd think the pricing of the ToD plan versus the fixed rate plan has more to do with how the power company themselves has to buy power and model/hedge against demand at various parts of the day, rather than simply trying to make the costs even for the consumer.
> FWIW I'd think the pricing of the ToD plan versus the fixed rate plan has more to do with how the power company themselves has to buy power and model/hedge against demand at various parts of the day, rather than simply trying to make the costs even for the consumer.
I agree, my single point of evidence to support my theory is that the power company advertises the TOD plan by presenting an average customer with their breakdown of usage throughout a typical day, and then itemizes how it would look with flat-rate versus TOD. The resulting figure is within a few pennies. I figure that is not accidental, but I could be wrong and it is entirely coincidence.
Fair point.
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.
Not the parent, but here is one location that has rates in that range in the U.S.
https://casscountyelectric.com/rates
They are most likely not based in the US, but converting to USD to make comparison easier.
I am not in Quebec but Quebec hydro rate D for standard residential would be one example of around what I pay.
https://www.hydroquebec.com/residential/customer-space/rates...
Another example would be Manitoba hydro
All figures in Canadian currency
https://www.hydro.mb.ca/account/rates/residential/
Specifying USD is indeed often a service usually offered by people born elsewhere for people born elsewhere. Americans seem rarely know about these mysterious places, where bills can come in all sorts of funny sizes and colours. (kind of joking)
Which are those use cases, considering that the hypothetical server, as described by the GP, is also extremely slow (4/6 hours for a response)?
> I pay about $0.075 USD per kWh
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?
can send safely context if there’s confidential computing ala my site https://trustedrouter.com/
How do you prove you are running exclusively on Nitro enclave instances or GCP confidential spaces?
Seems clear from their website?
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.
Is your local compute airgapped?
My local compute is used by me, and I'm accountable to myself whether or not is secure. So to a certain degree, I trust myself and also know what limitations / potential vulnerabilities it might have.
I trust my local compute quite a bit more than a random project that has "trusted" in its name.
Great, so the other member of the set matters for you more than cost.
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?
Do I really need to? No, not really. The 27B full density, 35B MoE, 70B and 122B models I have in use get me 95% of the way there on a lot of things. Particularly when dealing with languages and systems where I have at least an intermediate level of knowledge on, to know whether something is going down a dead end, using a wrong method, metaphorically chasing its tail, or is producing valid output.
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.
>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?
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.
Very good description of it. It does seem like a bit of a rhetorical question to ask a forum that has a very high population of Linux and BSD users why they might desire to have the option to do something themselves rather than relying on an external packaged ready to go product.
The whole mentality of thinking one knows better than another about what they need causes infinitely more problems than it solves.
>and people will compromise speed for data sovereignty
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!
As someone who has worked in two industries that are at the maximal end of data sensitivity and privacy this comes across as a tinfoil hat issue not a real business requirement. In such cases we've always found ways to trade dollars for the privacy we need without having to run our own inference at excruciating slow speeds.
Do you mean by trading dollars for the privacy you need as:
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.
A) is very doable with e.g. Amazon Bedrock.
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?
It is not my use case but given recent political developments in international relations caused by the executive branch of the US government, off the top of my head, I could think of a lot of European or Canadian firms for which that would not be an option. No matter what they might promise about European sovereignty. For a good 'ol patriotic US domestic company? Sure.
Yes, its the US cloud act risk EU companies run up against on hyperscalers like MS/AWS.
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.
It's also worth considering what you are actually paying for. And it's not keeping the data private, it's taking the blame when there is a breach. Same reason companies hire big consulting firms whenever they need to make an important but possibly risky decision.
>very doable with <US company>
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.
There are regulated sectors in countries where data sovereignty is important enough that the sector sticks to air-gapped on-prem hardware and does not use cloud services at all. They have the dollars to pay for more than what it would cost to run on the Cloud.
Having worked in / adjacent several such industries, a lot of the question depends on scale.
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 $$$ on their models, but not at the level of high-security.
> Worst-case example: Bootstrapped startup working in military.
That's the easiest case.
AWS Bedrock models running in AWS Secret Cloud for Industry. (I really have no affiliation with them, I'm just like... this is a completely solved problem, why do people think this is hard and requires on-prem hardware?)
https://www.aboutamazon.com/news/aws/aws-secret-cloud-for-in...
I'm with GP that these are tinfoil hat concerns, when there are solutions to all of these, unless you're perhaps in some country with very specific needs beyond things like European sovereignty or US military secrets (like a non-US defense concern).
You seem to be categorizing everything that considers their data being in the possession of the US an unacceptable risk to be tinfoil hat, which is kind of an insult to a large portion of the world. If you haven't been paying attention to the news in the last 48 months, the political reality has shifted considerably.
Note that the other commenter never said US-based military oriented startup. You just assumed, then jumped to "heck yeah let's use Amazon Secret Cloud for Industry"
Not everyone has or wants an office in Crystal City.
> Omitting Azure, which gives some privacy for some $$$ on their models, but not at the level of high-security.
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.
At least for regulated applications I worked on, no one cared.
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.
The expectation that one BigTech company has a competent security team while the other doesn't seems entirely baseless?
There are situations where you don’t care about government spying but care about random criminals And there are situations that are the opposite
Interesting. So nobody would have had a problem with you running stuff on Chinese AI providers?
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.
It's completely academic. At 5tok/s you can process 13 MTok per month at concurrency 1. I use 5 BILLION tokens per week when coding.
Yeah. At 5 tok/second, you're talking about around $195 worth of output tokens per month. There is no way I can run a usable K3 model for $195 a month of capex, opex, or any-kind-of-ex.
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.
There are a number of use cases where sending the contents of your context and prompts (and the resulting output) to a 3rd party service is off the table as an option, and people will compromise speed for data sovereignty.
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.
> Are there? At the highest levels of defense and law, AWS and Azure are used.
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-...
Yeah, true European cloud providers for these kinds of things seem to be behind, and a lot of the ones offering data compliance at the level of AWS are small enough that it's a bit harder to trust they'll be around and will keep their promises.
Hopefully that changes!
I’ve priced it out: max $135/month to run a dual Xeon 2U server with 3T RAM & 2x 22 core Xeon Gold. It’s the 2x 750W power supplies that ultimately determine opex. My power costs $0.124/kWh, the $135 assumes drawing maximum power continuously, and in that case, I can probably offset my heating bill a little bit in the winter, so maybe effectively a little bit lower.
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.
Keep in mind that just because it has dual 750W power supplies that doesn't mean it's what its load will be, for a full CPU loaded wattage figure you'd need basically a pair of kill-a-watts plugged in inline on the feed for each poewr supply and then run stress-ng with artificial cpu stress on all cores for an hour.
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).
If you actually were running this thing at 80% or 100% load, then the first thing you'd want to is get a better PDU and then connect your servers to that (48V DC).
One of the problems in buying used/refurb x86-64 rack servers for test and development/proof of concept environment, is that by volume in the market, there's not that many -48VDC power supplies going around, because maybe 5-10% of enterprise customers buy them. Resulting in many fewer units ending up on the resale market.
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.
(Context: Parent comment was edited after I wrote this comment)
Where in the world are you finding that much RAM in a racked server for $200/month?
I think he means electrical bill at his estimated wattage load of the server and his known kWh cost, not rented server/hosting cost.
Aha, right. That makes a lot more sense.
At my house. I have 5Gbps fiber and could pay for 10 or 25 if I need it.
Gotcha. But to be clear, you’re talking only about energy usage, correct?
Yes, what other opex is there? It will have good ventilation, I’m not worried about cooling.
One aspect of this is cyberattack proliferation by way of "Hey boss, I saw this TikTok that says if you let me invest [a tiny piece of the neighborhood's profit|our militia's budget] into some RAM, I could get a fully autonomous cyber operation up and running that pays for itself via ransomware etc. within weeks. You like it, we upgrade to something that can work even faster. We don't need the hacker guy from Swordfish with fifty monitors, we just need my cousin who likes building gaming PCs."
That's a world that I don't think we're ready for.
A similar world is already here.
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.
I dunno, K3 thinks a lot before it actually replies, and you might be in the ~1 tok/speed region or even "seconds / tokens", and with K3, you'd wait days if not weeks for a reply in that case.
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.
I agree it's very likely to be painfully slow, I very much want to see some real world results from people who try it. Early testers will inform others on whether it's even worth trying. Results very much TBD right now. I don't have a system sitting around here with 2TB of greater of RAM that isn't already committed for other uses, regretfully.
Lets say an easy response takes 32k tokens in total, and to be generous, let's say it does 1 tok/s. This is already ~9 hours, and 32k reasoning tokens isn't even that much and as mentioned, K3 probably does the longest/most reasoning/thinking out of the available open weights models today, much like GLM. Just lowering that performance to 0.5 tok/s, would lead to ~18 hours for a simple prompt to receive an answer.
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 :)
You can rent one in the cloud to try it
They are saying that AMD's new Epyc Venice CPU has 16 memory channels allowing up to 1.6Tb/s of bandwidth. Which is higher bandwidth than most non-HBM GPUs.
So full CPU local AI inference may become viable option in coming years.
the GPU competition is using 16 gpus, so the actual comparison is that the CPU has <1/10th the bandwidth
This is essentially guaranteed. There are lots of useful smaller models that we should be able to run locally. Over time they'll be more and more capable and require less API usage.
Im wondering if we are finally seeing the end of the "hard disk" era, and are entering a new era of vast instant on systems.
> running it on a no GPU, but tons of RAM server
Or from SSD using something like Colibri[1]. Not going to be quick, but at least runable.
[1]: https://github.com/JustVugg/colibri
It's a great concept but I think it would cross the line from 'very slow' to 'so slow it's unusable' at this size. Even if we say you have an NVME SSD that does 7GB/s reads, that's dramatically slower than being able to hold the whole thing in DRAM. Like the difference between 1.3 tok/s in RAM vs 0.1 tok/s with a colibri-like method.
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.
For single stream inference of a MoE model, the size of active sparse parameters will matter a lot more than total parameters. This is generally around half of the reported active parameter count - the other half being a dense subset that can be easily cached in VRAM even on fairly modest consumer setups. So the achievable performance may be quite a bit better than a naïve assessment might suggest.
On a server machine you can have more than 100GB/s of NVMe if you parallelize (RAID 0 and the like). But it's still gonna be noticeably slower.
1536GB of DDR4 ECC server RAM is somewhere between $4000-6000 USD used right now, by the time you put in parallel enough NVME SSD to approach good speeds, you'd be approaching that (and also likely running out of PCI-E bus lanes directly attached to the same motherboard to reasonably do so).
It claims to support using multiple devices RAID-0 style, which should boost performance, but yea probably not very useful for most.
But still fun you can run it at home.
Won't the answer (even for a pretty basic message like "hi") at SSD speeds take like a _entire week_ to _start showing useful output?_ (attempting to do 22k average claude code system prompt + 32k thinking tokens thru 0.1t/s throughput)
> 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.
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.
Update: Looks like the model is larger after all (1561.44 GB). Only the MoE weights are MXFP4, while the other weights are BF16 (and a few FP32).
* 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.
As you already went through the thought exercise of laying all this RAM over various slots, then match against the right CPU (which also you'll need multiple) - it becomes clear quite fast that it's trying to mimic the architecture of a GPU except in extremely low fidelity and bandwidth @ a higher energy cost.
It will be not 5 tok/sec. More like 0.5 tok per sec with a fast cpu setup.
> Even if the output is like 5-6 tok/s
On a 3T model I’d imagine you’d be closer to 0.05 tks
Presumably it’s MoE and only needs to read a small fraction of the weights per token. Bonus points if you can get decent speculative decoding without becoming ALU-limited.
Speculative decoding is not really worthwhile for sparsely-loaded models. You end up paying in both memory bandwith and compute (loading experts based on wrongly-predicted tokens) which leaves you worse off overall. It becomes viable (even for sparse MoE) once you're batching so widely that you end up having to load most of your total weights anyway.
> Speculative decoding is not really worthwhile for sparsely-loaded models.
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.
> If wonder if you can train a model to optimize this, by trying to make the expert selection sticky across a few tokens
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...
Those old LTT videos of high core-count threadrippers running GPU benchmarks become more relevant each day.
The performance bottleneck is not really so much the number of cores or processing power in each core, but the memory bus bandwidth to/from the CPU. I have an older dual socket xeon server here which is a CPU-only LLM test machine with 256GB of RAM and the actual CPU stress is not much, I can even quantify this by how little it spins up the CPU fans to meet thermal load (the CPUs are operating at nowhere near their 180W per socket max capacity, compared to like, crunching prime numbers or running cpuburn).
But the memory bus speed is fully committed when generating tokens or thinking.
Thats where the threadrippers really excelled. They had the lanes for memmory access. We might soon see the return of dinner plate-sized CPUs with thousands of pins.
The epyc Venice SP7 socket is apparently 9324 pins
https://x.com/tomshardware/status/2066846693778510331
We are going to need a bigger boat.
https://www.cerebras.ai/