Probably won't have to wait that long. Prism released Bonsai 27B (https://huggingface.co/prism-ml/Ternary-Bonsai-27B-mlx-2bit) as a ternary model a few days ago, its just ~7GB and runs at 44+ t/sec on an m4 max laptop. That's already in the ballpark of active parameter count of most 200B+ models, so we will get a model like this whenever Prism feels like releasing one.
It is debatable if we will actually need that many parameters though, since recursive nets like HRM (https://huggingface.co/sapientinc/HRM-Text-1B) don't need to parametrize as heavily.
agreed!! in my heart i really wanted to say by the end of 2026 but wanted to add some wiggle room in case they start to ban open source AI development.
I mostly agree with the prediction though maybe a bit more pessimistic about the timeline. Also I'm not sure our current usage of parameter count would make sense in this scenario, such a feat would require compressing current parameters in a manner much different then something producing a bit count per parameter. A hypothetical example would be using a single seed parameter per layer which then passed into a noise function produces the functional weights for that layer, able to reduce per weight size to sub bit levels (256 bit seed, producing 16K weights).
But you said 7-9 tokens/second, that's not a decent speed. I'm not an expert by all means but in my local experiments, less than 12 to 16 tps is too slow.
You think of an idea that you want to have the LLM process, queue it up, and go back to what you were doing. Once you've finished reading the next article on HN about a 5 tps Xeon, your task will be complete. It's kind of like using a 3D printer: It doesn't matter if a print takes 10 hours, because when you come back in the morning it will be done.
Yes, with top-tier GPU farms you can hit hundreds of tokens per second. But if the old Xeon in the closet can get useful work done at 5 tokens per second, there are lots of people and lots of use cases where a free, unlimited 5 TPS stream is worth more than paying a dollars per day to get access to a 500 TPS source.
Once you've used a model that runs at hundreds of TPS, it's hard to go back. Everything completes so quickly that you can iterate without breaking out of flow state. My biggest gripe with slow (<50tps) LLMs is that I've lost all the mental context I built up by the time it's done, which makes it extremely difficult to explore or iterate on solutions.
It's still the same thing, you can ask it to do a full on report give explanation and details be thorough and then go do something else, another task a lunch break whatever and it will be done when you're back
This is like comparing a hammer to a screwdriver and feeling smug because you can hammer nails faster than someone else can drive screws.
These are fundamentally different tools for entirely different applications. They only look similar to people who don't understand the tools or their purpose.
> Once you've finished reading the next article on HN about a 5 tps Xeon, your task will be complete.
If I spend 10 minutes reading an article, that would only generate 3000 tokens.
That’s not counting the prompt processing time.
We have very different expectations for LLMs if your tasks only take a couple thousand tokens and you’re happy waiting 10 minutes for it.
> Yes, with top-tier GPU farms you can hit hundreds of tokens per second
My 5090 gets hundreds of tokens per second with this model. No farm needed. I’d have to double check but I think even a $1000 Intel B70 might break 100 tokens per second.
> But if the old Xeon in the closet can get useful work done at 5 tokens per second, there are lots of people and lots of use cases where a free, unlimited 5 TPS stream is worth more than paying a dollars per day to get access to a 500 TPS source.
If that old Xeon pulls 200W from the wall and you pay national average electricity costs, it’s going to cost $0.90 per day to run it.
I would rather pay a dollar per day, get my answers 100X faster, and not have an old Xeon heating up my house.
Except often queued agentic flows must be checked in on. Or to use the comparison, 3D printers are not immune to making spaghetti all night when something goes wrong. (I’m not a 3d printing expert so maybe that is solved now)
It is common for agents to just stop because overload or some API error hijinks.
Or you get a TUI question that is blocking.
In general you’re right though, staring at tokens from agentic is not time well spent.
Some of these I’ve built custom harness around in iterm2 though.
For me, at least for agentic use, you need at least ~40tps. Less might be good only for tasks you could run in the background (like at night maybe).
Instead of coding agents like tool for local models I would like to see more "docker ps" like tools where you can queue up tasks that get processes incrementally (maybe when the pc i idle) and are specialized for doing retries and caching as much work as possible to work decently with slow local models.
Yep, and we don't even know how long they spent on prefill. A typical 50-100k token session could take 10-20 minutes to prefill on a Mac.
Pending any new hardware or radically different LLM architectures, we're going to be waiting a lot longer than 2027 for 200B models on local hardware. SOC platforms like Apple Silicon are hamstrung by their obsession over raster performance, the hardware lacks the fundamental GPGPU hardware to be a replacement for real-world inference.
> I have a prediction. By the mid of 2027, we will have >200B MoE models running on basic consumer hardware.
This prediction alone isn’t useful at all without a bound on speed and maybe quantization.
You can already run >200B MoE models on basic consumer hardware by picking a low bpw quantization and then streaming the experts from SSD. There have been a lot of proof of concept demos, but nobody uses them because they’re so slow and the quality is so degraded.
If you’re saying that hardware will catch up by mid-2027, I disagree. The limitation is fast memory and that’s going to be expensive for a while. I have a 128GB unified memory machine that can technically run 200B MoE models with enough quantization, but it’s so slow that there’s no reason to do it. It’s going to be a few years before we have enough RAM and processing power in basic consumer hardware without spending as much as a used car to get it.
> This is a GPT4 level model running locally with a decent speed on a 16gb ram macbook air.
Sorry, but 9 tokens per second with a slow prompt processing speed is not decent for anything other than getting short chat responses.
You’re also not running the full GPT4 quality model. I’m very familiar with that model from some other work and the 4-bit quants are just not as good as all of those KL divergence plots would have you believe.
You also have very short context. It’s basically useless for anything more than short chats where you’re okay watching the output come back at reading speed, skipping the reasoning part (which is important for calling it GPT4 level quality), and waiting a long time for the first token.
Yes, it’s technically running, but not in a way that would be useful by normal LLM standards.
>>Yes, it’s technically running, but not in a way that would be useful by normal LLM standards.
What are the LLM standards?
Do you know how many people use perplexity? I know many people who are not software engineers or tech workers and have a LLM subscription for rewriting their stuff (non-native english speakers) in english. There are many use cases for running good models locally. Maybe not for you, but someone might find this beneficial.
i have been optimizing for that. for now samosa is capped at using half of the avaiable cores and switching between them, which keeps the system 'less hot' as it would have been. i will also release better thermal control in the next release. at this point its basically sacrificing about 20% of the speed to keep the hardware less stressed (and hot).
>>It's quite telling you didn't use Qwen3.6-35B-A3B locally to build that
that would have run into a race condition unfortunately ;)
but there is a sample landing page + a python function on the repo which shows what the model produced. my goal is to integrate the local model in my workflow so that claude/OAI can call this model for basic stuff.
Unless there are major improvements to how much hardware it takes to run a 1T model, this is deeply unrealistic. First because why release hardware that puts your biggest customers (data centers) out of business. Second because as I understand it the data centers have bought up all the high end chip production capacity for at least the next year and unless the bubble pops that'll continue for a while.
I tried Qwen3.6-35B-A3B, but it couldn't generate a 50-100 line Clojure file without having broken parens mismatches. I know Clojure isn't super popular, but the syntax is pretty simple and the frontier models do fine with it.
I'm not lamenting that they aren't close, I'm saying Qwen will frequently output code that isn't even syntactically correct, even when the syntax is simple. Which makes it unusable for coding.
Yeah prediction models and many parentheses are probably not a good combination, but we're not talking about anything exceptionally complicated here. I have had syntax issues in Python as well.
Some ppl don't like to hear it. But I would assume that token costs when using an inference provider are cheaper than electricity of using locally.
If we just take into account output token generation for simplicity. With 5tps u get 18k tokens an hour. That would costs around 0.005USD from an inference provider.
I estimate that the server consumes probably around 500W during inference.
In Germany where 1kwh cost around 0.3USD, 18k tokens inferred locally would therefore cost 0.15USD which is 30x the costs of using an inference provider.
But for ppl who worry about their data, running locally might still be good. However, they should be aware, that it is much less efficient than using an inference provider.
The efficiency gap will also significantly increase as new GPUs will make inference much more efficient.
EDIT: I first thought it'd be 180k token, but thanks to someone mentioning in the comments, it is 18k. I guess with that, it will be tough unless u got electricity almost for free. Also, the inference providers are probably still using H200/H100 for those small models. Once they use GB300 or next year the new Ruby GPUs, inference will be cheaper by a factor of 30. By then, running local models will mostly be about privacy.
I don't pay anywhere near 0.30usd in the US - I pay half that off peak and can buy 1000$ worth of batteries to load up on super off peak (0.11usd). Also the inference providers are fighting over market share with huge debt loads so they are definitely going to go up in price.
Inference costs will go down massively once they use the upcoming GPUs. I estimated that a model like GLM5.2 will be around 0.03USD/M output tokens in 2 years when the Feynman GPUs will be available in 2028. And this did not even consider architectural efficiency improvements. In mid 2027 we will already see a 10x reduction once everyone has switched to the Ruby architecture.
It will be feasible for everyone to have 20 different agents running at all times. A new world is coming
It's all relative. On the opposite coast, Maine it is ~ $0.28 cents kwh including getting it there. (~ 50% energy, 50% delivery). It's too darn expensive here.
Of course efficiency matters, but a lot of people either have cheap electricity or efficient hardware. My AMD strix halo home server can serve Gemma4-26B at like 70 TPS (rough estimate, I don’t remember the exact speed buts its fast af) while only using 100W.
don't care, and yeah i don't like to hear it. we don't run local because it's cheaper money wise. we do it for freedom, for privacy and having option makes it cheaper in the long run. if there was no local options, your cloud model would cost much more!
It's the "Race-to-Idle" situation all over again. It consumes less power to complete a task faster, whereas using "low power" hardware that draws max TDP for 30 minutes isn't very power efficient.
The privacy nuts have a better leg to stand on, but even then it's hard to believe that they're using on-prem AI to replace SOTA model inference. As cool as local LLMs are, a lot of the stuff people run is a novelty.
Have you tried with a single CPU to get rid of the NUMA penalty? I understand this likely means halving the memory but I am interested in how much of a difference it makes
I have (192GB machine with two CPUs), pretty much does the trick. It just runs some small models used for embedding, etc. and has those on one CPU / memory node and all the Docker containers on the other one.c
I have a dual xeon also, same as OP: Ivy Bridge + 128GB DRAM, and was never really able to get decent LLM performance out of it. So I ended up biting the bullet and adding a "budget tier" A4000 20GB GPU. Too bad all my DRAM is wasted now--not sure if there is a way to take advantage of lots of DRAM once you move over to having inference happening on the GPU.
The 5.2 tokens per second generation is not that bad, what kills it is the 16.2 prompt processing that makes this too slow to consider even if you have the hardware lying around.
A dual Xeon of this era is probably pulling 300W or more when loaded.
At national average electricity prices, that’s $1.35 per day. More during the summer if you have to cool the space.
If you run it 24/7 and ignore prompt processing time (not a good assumption at all) it would get around 400,000 tokens in a day.
That’s about $0.30 per million output tokens.
Coincidentally, that’s the same price for this model on OpenRouter right now, but OpenRouter token gen will be 8X faster.
There are a lot of good reasons to experiment with running LLMs locally, like if you don’t want any data leaving your house.
Don’t think that you’re going to come out ahead monetarily. I say this as someone with a lot more money invested in local inference hardware at home. It’s fun, but it’s not a way to save money.
Reasonable analysis, especially because this person seems to have an actual house. In my case, I rent and don't pay for electricity directly, so the cost effectiveness threshold is whenever the landlord starts complaining
I think, may be actually wrong, that most of us do not consider running a model locally a way to save money. It is a way not to spread personal info around.
Anyone running LLMs at home will come to that realization quickly, if they’re looking at their power bills. Even feeling the heat output of a computer running at 100% in your office makes it clear.
I was responding to a lot of the comments saying this was a reasonable way to avoid paying for tokens or subscriptions. I don’t want anyone getting the wrong idea that this is a way to save money if that’s their priority.
> Even feeling the heat output of a computer running at 100% in your office makes it clear.
What does it make clear? That I can replace the space heater my wife runs 9 out of 12 months of the year with a home server? And effectively get $0.00 per token during those times?
In houses running A/C year round, sure there'd be some impact, but in all the places running heat, doesn't seem that it'd move the needle on power bills.
There are startups whose entire business model is "cloud server as a home space heater" (aka "data furnace") ...
I love my little dual core X99 board with Xeon E5 2673 V3. It's not power efficient, but I just leave it in my basement for local Jupyter Notebook stuff. Much faster than everything cloud-based for a reasonably price at my scale.
I run the same setup Gemma 4 26B on a 2013 Mac Pro (dual graphics cards but they're useless for this). I also get about 5 t/s. It's perfectly serviceable for some tasks!
The transformer architecture is fundamentally unsuitable for local inference, while being efficient at scale. It's a fun experiment to try, but that's about it.
Is it just me or does this post not mention how much RAM they had? I would love to know - I have a dual-Xeon 1U screamer with 96GB of DDR4 RDIMM just sitting around...
FWIW I'm getting a hardware max of 20 tok/s (approx topping out the GPU's compute) on my custom local diffusiongemma port running on an M3.
Such a system is RAM bandwidth limited and not compute limited Switching to q4 from q8 would decrease the amount of data needing to be loaded by half. The token generation rate would nearly double. But generally if you can do q6 or q8 and you have enough RAM you really should. Even if it's slower.
To me context means everything.
Tokens per second is a great metric but in the real world context window is the deal breaker when a real use case is on the table.
Author here. The short version: a viral post ran Gemma 4 on a 2016 Xeon; my Xeons are 2013, and the fork it used assumes AVX2, which Ivy Bridge doesn't have. The build failure was easy. The fun bug was the silent one: two MoE graph ops with no dispatch case on non-AVX2 builds, so every expert FFN output was uninitialized memory. Deterministic, NaN-free, fluent-looking multilingual gibberish.
The fix is open upstream as PR #2138 (https://github.com/ikawrakow/ik_llama.cpp/pull/2138), awaiting review. Fair warning on the AI angle: the patch was written by Claude at my direction. The post is explicit about which parts were me and which weren't. Happy to answer questions about either the bug or the workflow.
Here's the thing: life also imitates art. If you invert your load-bearing assumption, it could be that he just reads too much slop. But my honest take? You might be right.
Apologies for asking here but literally nobody knows:
Android studio connected to a local model disconnects automatically after 10 minutes. How set this limit to 12 hours or remove it completely?
I could run my LM studio model all night... but I cant, since Android studio times out after a hard limit of 10M.
This is not related to number of tokens.
I tried Googling, searching for settings in Android studio, even created a stackoverflow post - but zero information. Jetbrains mentions "remote agent timeout mechanism" - but after changing it, nothing happens.
If the local model is served via ollama, there's a default timeout of 10 minutes , which can be adjusted either per-call , or (as I did) in the systemd service environment variables
Thank you for your reply. I use LM studio (local server), but can switch to a different tool.
Do you know how to switch it in LM studio?
What I see is that: android studio gives "Error: stream failed" and in LM studio server I see it is still working, then says that client (=android studio) disconnected.
Dunno, I have not used either of those. (Had been using zed and ollama, and ollama had plenty of odd defaults that needed fixing)
Glancing through the docs, I would be digging down in the config of both Android studio and lm studio for either a TTL or jit auto evict setting, and if you find it, set it to some large number measured in hours?