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This is why it’s so critical to have open source models. In a year or so, the open source models will become good enough (in both quality and speed) to run loc
by g42gregory 1y ago
This is why it’s so critical to have open source models.
In a year or so, the open source models will become good enough (in both quality and speed) to run locally.
Arguably, OpenAI OSS 120B is already good enough, in both quality and speed, to run on Mac Studio.
Then $10k, amortized over 3 years, will be enough to run code LLMs 24/7.
I hope that’s the future.
- okdood64 1y agoWhat's performance of running OpenAI OSS 120B on a Mac Studio as compared to running a paid subscription frontier LLM?
- andrewmcwatters 1y agoChiming in here, M1 Max MacBook Pro 64GB using gpt-oss:20b over ollama with Visual Studio Code with GitHub Copilot is unusably slow compared to using Claude Sonnet 4, which requires (I think?) GitHub Copilot Pro. But I'm happy to pay the subscription vs buying a Mac Studio for now.
- Jimpulse 1y agoOllama's implementation for gpt-oss is poor.
- jermaustin1 1y agoI will answer for the 20B version on my RTX3090 for anyone who is interested (SUPER happy with the quality it outputs, as well). I've had it write a handful of HTML/CSS/JS SPAs already. With medium and high reasoning, I will see between 60 and 120 tokens per second, which is outrageous compared to the LLaMa models I was running before (20-40tps - I'm sure I could have adjusted parameters somewhere in there).
- ivape 1y agoDo we know why it’s so fast barring hardware?
- mattmanser 1y agoBecause he's getting crap output. Open source locally on something that under-powered is vastly worse than paid LLMs. I'm no shill, I'm fairly skeptical about AI, but been doing a lot of research and playing to see what I'm missing. I haven't bothered running anything locally as the overwhelming consensus is that it's just not good enough yet. And that from posts and videos in the last two weeks. I've not seen something so positive about local LLMs anywhere else. It's simply just not there yet, and definitely aren't for a 4090.
- ivape 1y agoI guess I meant how is a 20b param model simply faster than another 20b model? What techniques are they using?
- medvezhenok 1y agoIt's a MoE (mixture of experts) architecture, which means that there's only 3.6 billion parameters activated per token (but a total of 20b parameters for the model). So it should run at the same speed that a 3.6b model would run assuming that all of the parameters fit in vRAM. Generally, 20b MoE will run faster but be less smart than a 20b dense model. In terms of "intelligence" the rule of thumb is the geometric mean between the number of active parameters and the number of total parameters. So a 20b model with 3.6b active (like the small gpt-oss) should be roughly comparable in terms of output quality to a sqrt(3.6*20) = 8.5b parameter model, but run with the speed of a 3.6b model.
- deleted 1y ago[deleted]
- jermaustin1 1y agoThat is a bit harsh. I'm actually quite pleased with the code it is outputting currently. I'm not saying it is anywhere close to a paid foundation model, but the code it is outputting (albeit simple) has been generally well written and works. I do only get a handful of those high-thought responses before the 50k token window starts to delete stuff, though.
- mockingloris 1y agoMost devs where I'm from would scrape to cough up that amount More niche use case models have to be developed for cheaper and energy optimized hardware. └── Dey well
- skybrian 1y agoThis would be a business expense. Compared to hiring a developer for a year, it would be more reasonable. For a short-term gig, though, I don’t think they would do that.
- asadm 1y agoEven if they do get better. The latest closed-source {gemini|anthropic|openai} model will always be insanely good and it would be dumb to use a local one from 3 years back. Also tooling, you can use aider which is ok. But claude code and gemini cli will always be superior and will only work correctly with their respective models.
- SparkyMcUnicorn 1y agoI use Claude Code with other models sometimes. For well defined tasks that Claude creates, I'll pass off execution to a locally run model (running in another Claude Code instance) and it works just fine. Not for every task, but more than you might think.
- asgraham 1y agoI don’t know about your first point: at some point the three-year difference may not be worth the premium, as local models reach “good enough.” But the second point seems even less likely to be true: why will Claude code and Gemini cli always be superior? Other than advantageous token prices (which the people willing to pay the aforementioned premium shouldn’t even care about), what do they inherently have over third-party tooling?
- nickstinemates 1y agoEven using Claude Code vs. something like Crush yields drastically different results. Same model, same prompt, same cost... the agent is a huge differentiator, which surprised me.
- asgraham 1y agoI totally agree that the agent is essential, and that right now Claude Code is semi-unanimously the best agent. But agentic tooling is written, not trained (as far as I can tell—someone correct me) so it’s not immediately obvious to me that a third-party couldn’t eventually do it better. Maybe to answer my own question, LLM developers have one, potentially two advantages over third-party tooling developers: 1) virtually unlimited tokens, zero rate limiting with which to play around with tooling dev. 2) the opportunity to train the network on their own tooling. The first advantage is theoretically mitigated by insane VC funding, but will probably always be a problem for OSS. I’m probably overlooking news that the second advantage is where Anthropic is winning right now; I don’t have intuition for where this advantage will change with time.
- hoppp 1y agoI am looking forward for the AMD 395 max+ PCs to come down in price. The inference speed locally will be acceptable in 5-10 years thanks to those generation of chips and finally we can have good local AI apps.
- skybrian 1y agoOpen source models could be run by low-cost cloud providers, too. They could offer discounts for a long term contract and run it on dedicated hardware.
- qingcharles 1y agoThis. Your local LLM, even if shared between a pool of devs, is probably only going to be working 8 hours a day. Better to use a cloud provider, especially if you can find a way to ensure data security, if that is an issue for you.
- wongarsu 1y agoExactly. There is no shortage of providers hosting open source models with per-token pricing, with a variety of speeds and context sizes at different price points. Competition is strong and barriers of entry low, ensuring that margins stay low and prices fair. If you want complete control over your data and don't trust anyone's assurances that they keep it private (and why should you) then you have to self-host. But if all you care about is a good price then the free market already provides that for open models
- hkt 1y agoHetzner and Scaleway already do instances with GPUs so this kinda already exists
- hkt 1y agoIn fact, does anybody want to hire a server with me? I suspect it'll work out cheaper than Claude max etc: a server from hetzner starts at £220ish: https://www.hetzner.com/dedicated-rootserver/matrix-gpu/ https://www.hetzner.com/dedicated-rootserver/matrix-gpu/ It might be fun to work out how to share, too. A whole new breed of shell hosting.
- benterix 1y agoI have a couple of non-GPU servers with them and quite a few Heztner Cloud projects, but I never understood their GPU offering. They have just two (small) VRAM sizes and you pay per month, whereas the ones like Runpod have a large selection of whatever you need, and they are cheaper, and you can rent them for a shorter period like two weeks, no setup time. Am I missing something?
- aydyn 1y agoThis is unrealistic hopium, and deep down you probably know it. There's no such thing as models that are "good enough". There are models that are better and models that are worse and OS models will always be worse. Businesses that use better, more expensive models will be more successful.
- hsuduebc2 1y agoI agree. It isn't in the interest of any actor including openai to give out their tools for free.
- lcfcjs6 1y ago[dead]
- seabrookmx 1y agoMost tech hits a point of diminishing returns. I don't think we're there yet, but it's reasonable to expect at _some point_ your typical OS model could be 98% of the way to a cutting edge commercial model, and at that point your last sentence probably doesn't hold true.
- aydyn 1y agoThere is cost/benefit analysis yes but fundamentally there are no diminishing returns because intelligence does not have diminishing returns. One of the key concepts in the AI zeitgeist is the possibility of superintelligence. There will always be the possibility of a more productive AI agent.
- ch4s3 1y ago> Businesses that use better, more expensive models will be more successful. Better back of house tech can differentiate you, but startups history is littered with failed companies using the best tech, and they were often beaten by companies using a worse is better approach. Anyone here who has been around long enough has seen this play out a number of times.
- root_axis 1y ago> In a year or so, the open source models will become good enough (in both quality and speed) to run locally. "Good enough" for what is the question. You can already run them locally, the problem is that they aren't really practical for the use-cases we see with SOTA models, which are just now becoming passable as semi-reliable autonomous agents. There is no hope of running anything like today's SOTA models locally in the next decade.
- cyanydeez 1y agothey might be passable, but there's zero chance they're economical atm.
- deleted 1y ago[deleted]
- jononor 1y agoGPU compute per dollar has been on a pretty steady curve of around 10x per decade. In ML for computer vision we were also able to make models around 10x as efficient per 10 years. I think with these two factors combined, mapped to LLMs, we will be able go match the performance of say Sonnet 4 on a 2000 USD workstation well within 10 years from today.
- root_axis 1y agoEven with 10x more efficient models and GPU compute, hundreds of GB of VRAM will still be on the order of tens of thousands of USD for the foreseeable future.
- holoduke 1y agoProblem is that it really eats all resources when using a llm locally. I tried it. But the whole system becomes unresponsive and slow. We need minimum of 1tb memory and dedicated processors to offload.
- cyanydeez 1y agoIts not, capitalism isn't about efficiency; it's about lockin. You can't lockin open source models. If fascism under republicans continue, you can bet they'll be shut down due to child safety or whatever excuse the large corporations need to turn off the free efficiency.
- 6thbit 1y agoMany of the larger enterprises (retail, manufacture, insurance, etc) are consistently becoming cloud-only or have reduced their data center foot print massively over the last 10 years. Do you think these enterprises will begin hosting their own models? I'm not convinced they'll join the capex race to build AI data centers. It would make more sense they just end up consuming existing services. Then there are the smaller startups that just never had their own data center. Are those going to start self-hosting AI models? And all of the related requirements to allow say a few hundred employees to access a local service at once? network, HA, upgrades, etc. Say you have multiple offices in different countries also, and so on.
- deleted 1y ago[deleted]
- g42gregory 1y agoEnterprises (depending on the sector, think semi manufacturing) will have no choice for two reasons: 1. Protecting their intellectual property, and 2. Unknown “safety” constraints baked in. Imagine an engineer unable to ran some security tests because LLM thinks it’s “unsafe”. Meanwhile, VP of Sales is on the line with the customer.
- nunez 1y ago> Do you think these enterprises will begin hosting their own models? I'm not convinced they'll join the capex race to build AI data centers. It would make more sense they just end up consuming existing services. they already are
- physicsguy 1y ago> manufacture They're much less strict than they were on cloud, but the security practices are really quite strict. I work in this sector and yes, they'll allow cloud, but strong data isolation + segregation, access controls, networking reqs, etc. etc. etc. are very much a thing in the industry still, particularly where the production process is commercially sensitive in itself.
- moritzwarhier 1y agoAfter trying gpt-oss:20b, I'm starting to lose faith in this argument, but I share your hope. Also, I've never tried really huge local models and especially not RAG with local models.
- jvanderbot 1y agoIt's not hard to imagine a future where I license their network for inference on my own machine, and they can focus on training.
- coldtea 1y ago>In a year or so, the open source models will become good enough (in both quality and speed) to run locally. Based on what? And where? On systems < 48GB?
- habosa 1y agoEvery business building on LLMs should also have a contingency plan for if they needed to go to an all open-weights model strategy. OpenAI / Anthropic / Google have nothing stopping them from 100x-ing the price or limiting access or dropping old models or outright competing with their customers. Building your whole business on top of them will prove to be as foolish as all of the media companies that built on top of Facebook and got crushed later.
- OfficialTurkey 1y agoCouldn't you also make this argument about cloud infrastructure from the standard hyperscaler cloud providers (AWS, GCP, ...)? For that matter, couldn't you make this argument about dependency your business has which it purchases from other businesses which are competing against each other to provide it?
- matusp 1y agoIn general, you are right, but AI as a field is pretty volatile still. Token producers are still pivoting and are generally losing money. They will have to change their strategy sooner or later, and there is a good chance that the users will not be happy about it.
- philistine 1y agoAre the cloud computing providers burning tens of millions of dollars each day and having to resort to NBA-player level salaries to recruit talent?
- pixl97 1y agoI mean in most large businesses the companies are performing risk analysis for exactly this.
- LikesPwsh 1y agoAWS/GCP are at least making money with their current pricing model. When your provider is dumping at a loss, it's their way of saying that the business plan is to maximize lock-in/monopoly effects followed by the infamous "enshittification".
- DiabloD3 1y agoWhy bother mentioning this model? From what I've seen, it only excels at benchmarks. Qwen3 is sorta where its at right now; Qwen3-Coder is pretty much at "summer intern" level for coding tasks, and its ahead of the rest. Shame anyone is actually _paying_ for commercial inference, its worse than whatever you can do locally.
- danny_codes 1y agoIMO local models is kind of inevitable. Hardware vendors will create efficient inference pcie chips and innovations in ram architecture will make make even mid-level devices capable of running local 120B parameter models efficiently. Open source models will get good enough that there isn’t a meaningful difference between them and the closed source offerings. Hardware is relatively cheap, it’s just that vendors haven’t had enough cycles yet on getting local inference capable devices out to the people. I give it 5 years or so before this is the standard