The awkward part is that software has a habit of racing toward free once it becomes good enough. If an open weight model delivers 95 percent of the value without recurring API costs, plenty of companies will choose that and spend the savings on integration instead. History keeps rhyming, even if investors hate the tune.
I ran DeepSeek and Llama and Mistral at home on my consumer grade gaming PC.
With a little tweaking of the system prompts and configuring web search, I was running a local LLM that felt pretty darn close to the commercial LLMs.
With this technology out in the open internet where you can download the models in a few hours I don’t see how the commercial AI companies are going to last. If selling “Artificial Intelligence” subscriptions is all your company does for revenue, you’re screwed.
I downloaded and ran an LLM that I could have a conversation with and feed basic coding problems to for basically zero dollars and ran it on my puny gaming machine…puny compared to enterprise-class hardware. It would be trivial for a company with a very moderate budget to buy some servers and start running their own LLMs that they can use to feed all the PII and HIPPA data they want.
And Llama and Mistral are ancient history at this point.
The cutting edge of local is lightyears better now. It’s basically where ChatGPT/Anthropic were not that long ago, with a bit less world knowledge because of the size.
What’s the cutting edge now? Skool me…I want to try it. Can I grab one using ollama?
https://sleepingrobots.com/dreams/stop-using-ollama/
And this is just the tip of the iceberg for ollama. They’re the same kind of scammy tech bros as OpenAI.
The best setup depends on your hardware. There is no “easy button” unfortunately, quantized LLMs are just too intense and finicky to run without making some informed choices.
It also depends on what you want to do with the LLM. For example, some are too slow or bad at long context for agenic use, some quantizations are great at scripts but terrible outside that, or vice versa.
But LM Studio and Qwen 3.5 35B Q4 is probably the “easiest” flat recommendation I can make.
Or… honestly, just pay $40 for basically unlimited usage for a year from an API, then roll your own frontend.
Well that sucks. I was really impressed as a novice to open weight LLMs with the ease of use for Ollama on Bazzite.
I’ve been running LM Studio on Bazzite and I had to do nothing to get it working. Just go to the LM Studio website and download the
.appimagefor Linux. If you open it with Gear Lever it will install like an app from the app store and show up in your launcher with an icon.From there I have just been able to download models and use them from in the app. In fact I setup a local server to connect to my IDE and have been trying out local models for coding. It’s pretty cool
Awesome! I’ll give it a try.
I can also vouch for lmstudio. If you can get Hermes running on Linux I would suggest trying that as well. It connects to lm studio and you use Hermes to communicate with the model. Iook into it as there’s a lot to it, I’ve really been enjoying using it so far it even learns how I like to create tasks and I’ve stopped having to ask it to delegate certain tasks, it just knows to do it and to break down the tasks so my fairly context starved local model can handle it.
As for a model, the Qwen 3.6 family of models do really well. I’d suggest the Qwen 3.6 35B a3b probably Q4 depending on your hardware. It’s large, but because it’s a mixture of experts model only 3b of experts are kept on vram at any one time so it stays fast. Qwen 3.6 27b is the smarter “dense” model, but trying to stay with Q4 for quality it becomes too large for 16GB vram and for me runs at like 2 tokens per second lol
Why are quantised LLMs harder to run than non quantised ones?
I just meant that you have to be cognizant of what went into the quantization.
As an example, a “Q4_K_M” could be too much quantization to be usable on one model, and an inefficient waste of space on the other. Two Q4_K_Ms of the exact same model could be completely different, one totally borked. Or one particular Q4_K_M could excel in one task, but be totally useless for another, even with the exact same settings, when a slightly different sized or type of quantization would excel.
It’s a deep rabbit hole. It’s not random either; there are distinct technical reasons behind every case mentioned above.
And that’s not even at the cutting edge quantization anymore, though what’s “cutting edge” completely depends on your particular hardware and use case.
I’m trying to make this sound daunting on purpose.
Many people have really horrible experience with a default “ollama run” for this exact reason, because the defaults are terrible and the customization is critical to getting coherent, performant output.
Unquantized LLMs, on the other hand, are basically always run the same way: vllm docker image on a big server, official weights. There’s less to “go wrong” trying to squeeze it on hardware with unofficial runtimes and compressors.
Since you mention using Ollama, you probably aren’t running actual deepseek on your pc. Ollama took a Qwen model that was finetuned using deepseek output and named it deepseek.
Those are pretty out of date models at this point. Right now, the model most people would recommend for consumer gaming hardware is Qwen 3.6 27b.
I actually tried Qwen 3.6 27B but it wouldn’t quite fit in my 6900XT so I had to go down to the 14B. I don’t have the tools or the skillset to really test the capabilities of an LLM but with some very rudimentary system prompts it felt quite natural to me. Shockingly natural considering that talking to a real LLM running on my own PC felt like it was smarter than the Majel Barrett computer on Star Trek:TNG…
The Western model was doomed to failure from the start. The only barrier to entry was being able to download thousands of TBs of internet archives/books and to have a lot of compute.
The math for these models isn’t proprietary and most CS students are exposed to machine learning and neural networks while in school.
The only advantage western companies had was the ability to buy up the entire hardware market, pricing out domestic competition, and to use their politicians to manipulate trade policy in order to restrict sales of critical hardware to China.
Every US tech company has dumped billions investing in an unsustainable business model with the hope of buying a global monopoly by strangling competition.
China can destroy all of that by making their models open weight. The real money is in finding and implementing custom AI solutions… not in charging for access to the models. By having freely available models, they’re making the barrier of entry as low as possible.
Not to mention that the insane amount of money being poured into hardware by US tech companies has created an environment where building fabs has a much shorter ROI, which also helps China’s development in that sector.
US companies are playing Monopoly while China is playing Civilization.
I still think this type of AI can lead to a worse state of living for most of us, but it’s better than the bullshit in the US.
Eat my ass Musk, Zuck, Altman and all you other fuck face tech bros
The localllama crowd has know this for years.
It happened faster than I expected, though; OpenAI/Anthropic hardly even got the chance to tighten the screws.
The article wastes no time getting to the underlying point in the very first paragraph:
Top executives at leading Western AI companies are increasingly warning about the safety and national security risks posed by Chinese open-weight frontier models. What they tend not to mention is that these models are improving rapidly and, because they are freely available, pose a serious threat to Western labs’ business models.
I found the following two paragraphs interesting:
By mid-2026, however, open-weight frontier models from Chinese labs such as Alibaba, DeepSeek, and Moonshot AI had nearly matched the leading Western models in intelligence and performance. Many companies have already begun building their AI systems on top of these free models, avoiding the high cost of closed-model APIs.
Because businesses can host open-weight models in their own private clouds, they can also avoid sending proprietary data to systems controlled by outside providers. Developers can fine-tune the models for specific needs, build applications and tools on top of them, and optimize them for their preferred infrastructure.
Back to local computing is the way to go
they can also avoid sending proprietary data to systems controlled by outside providers.
Not only proprietary but also personal or other kinds of sensitive data.
If you are doing health research on databases of personal health data, you should be able to guarantee the safety of that data.
That means you can’t use the current American systems, because they’ve been shown to be insecure.
This would be a major issue in EU, where such data is legally protected.This would be a major issue in EU, where such data is legally protected.
Nothing a EU–US Data Privacy Framework can’t handle.
I absolutely agree that that agreement is complete and utter bullshit.
Hopefully the shift there has been to achieve IT independence from USA will mean EU doesn’t give in so easy next time.
These people are too rich to be punished by laws. They’re the people laws protect not the ones they bind.
Not in EU, Eu has given fines to those big tech companies before and can do it again.
More than their profits? Their profits over course of the shittiness?
Being less bald face and trying to hide your corruption better than America doesn’t make you the good guy.
Fines are just the cost of doing business.
Wake me up when corporate charters are dissolved and executives are put in prison.
I just wish I could buy enough memory to run one of these models locally. Specially Kimi K3
How much RAM do you have?
I can run MiMo 2.5 at about 9 tokens/sec, on 128GB RAM, a 7800 and a 3090 in an SFF rig. That’s a fantastic 310B model. I’m requantizing it right now, to see if I can speed it up with Dflash.
Still fantastic models can be run on 64GB or 32GB CPU RAM, as long as you have some GPU. We’re awash in sparse models these days.
I’ve got 128GB RAM + 24GB VRAM on a 4090. I’ve managed to get a 400B parameter model running on a single board computer with 64GB RAM by using MMAP. But I want to run Kimi K3 locally so I would need a lot more RAM / bandwidth
Oh that’s perfect!
You can’t realistically run Kimi (as it’s a 1T+ model), but you’re set. There’s a glut of excellent 120B-300B models for you to choose from.
I’m quantizing MiMo 2.5 specifically to cram into 24GB/128GB as I type this. It’s tight, and will use up most of your memory, but it’s fantastically smart and plenty fast. The quantization won’t finish cooking until tomorrow, but I’ll upload it to huggingface then.
But there are others existing quants would fit, like Deepseek Flash IQ3_S: https://huggingface.co/unsloth/DeepSeek-V4-Flash-GGUF/tree/main/UD-IQ3_S
Or… well, any of these! https://huggingface.co/models?num_parameters=min%3A128B%2Cmax%3A256B&library=gguf&sort=modified
The key for you is to run the ik_llama.cpp fork: https://github.com/ikawrakow/ik_llama.cpp/
It’s specifically optimized for hybrid (CPU + GPU) inference on Nvidia desktops; you’ll get MUCH faster speeds than mainline llama.cpp or anything based on it. It also supports some more exotic quantization type; as an example, I’m quantizing MiMo 2.5 as a hybrid quant, with the dense layers at IQ6K/Q8_0 and the sparse experts as an IQ3_KT “trellis” quant type. This should yield a higher fidelity quantization than a typical Q3 GGUF while taking less RAM, at the cost of taking forever to quantize and a slight speed hit.
Some other quantization types (like the KS or R4 types) are specifically configured to be fast on CPU.
There are specialized “quant cookers” that make GGUFs specifically for ik_llama.cpp, like:
https://huggingface.co/ubergarm
https://huggingface.co/AesSedai
https://huggingface.co/sigargv/Laguna-M.1-GGUF
https://huggingface.co/models?other=ik_llama.cpp&sort=modified
Mine will be here once I upload it:
https://huggingface.co/Downtown-Case
For anyone else reading this: none of this is applicable to you if you have an AMD/Intel GPU, or an older Nvidia GPU, or less than a certain amount of RAM, or a non AVX2 CPU or… well, there’s all sorts of caveats.
The optimal runtime is different for everyone. As an example, exllamav3 is WAY better than llama.cpp on modern Nvidia GPUs until you get above a certain amount of CPU RAM; then using all that RAM for hybrid inference makes more sense. On AMD, different backends work better on different GPUs, and… well, you get the point. Basically all LLM running advice is irrelevant without specifics of your hardware, even this post will be obsolete in a month.
Same, getting ~3 trillion parameters in consumer hardware is rough.
If Nvidia has any foresight they’ll see the writing on the wall and start getting higher memory Spark style SMB inference machines, few people in the long run are going to pay retail API token costs,
Especially giving the lack of trust organizations rightfully have in big AI companies
Exactly, having the mustard toddler pull fable because of, who knows? That burned trust in the west’s AI companies.
And there is also the question of what they’re doing with the data you send into the models.
is their a meaningful difference between open weight and open source?
Open weight is analogous to a compiled binary. Similar to how Windows is closed source, but still runs in your own hardware, whereas Linux is truly open source.
Pretty big difference. An open weight model is a model that you can run on your own machine. You just download and it’s yours to host and use. You don’t need to have anyone host it on their own backend for you, the entire model is available to you to do that on your own. What you don’t have is any control over or access to anything related to how the model was trained. You don’t know what kind of data they used to train it, and how exactly they used that dataset. If you did, that’d be an open source model.
If all AI really is classified as a “weapon” for export-control purposes, well… in the US possession of weapons is a protected individual right. The US gov should be training open weight models and encouraging self-hosting.
Okay but what about the fact all of this is useless bullshit that will only make the world worse?
It’s far from useless. Overblown, for sure.
They made an awesome hammer that can solve many hammer-related tasks, but they’re selling it like it can also cook, drive and keep the house clean.
Blame the companies, not the math.
100%. These LLMs are fantastic tools for when you could use an LLM to solve a problem or make something better.
Using the right tool for the right job is really not a complicated concept.
The problem, as always, is marketing and hype noise caused by the people who see dollar signs.
Every time I hear about these hammer related tasks I find out they kind of suck or drained lake Tahoe to translate a book that already has a hundred translations.
Or the hammer related task is that one scene from ‘oldboy’.
