Broad and perpetual license over inputs and outputs, and even your name and profile picture.
Vague prohibitions on whatever may harm Z.ai’s "interests" or even the "national interests" of any country.
Vague prohibitions on "disturbing" or "inappropriate" content, whatever that is.
Vague prohibitions on discussing Z.ai, even my posting this comment violates it.
Can ban you if you, in the "sole and absolute opinion" of Z.ai, have violated these broad terms, and if you paid for the discounted yearly plan kiss your money goodbye.
I was looking at my token usage, mostly from subsidized codex/grok subscriptions and i'm a somewhat heavy user. The thing is i would actually use even more tokens if it wasn't for the weekly quotas.
In the end, with a $10k investment and running this kind of model, estimating a 2x increase in token usage because i wouldn't have weekly quotas and comparing to glm api prices, this thing could pay for itself in less than a year.
Obviously i'm paying subscription price right now, so the math doesn't work. Although using local ai removes all weekly quotas. Keep a subscription to have access to frontier models for planning work, and local hardware + glm-5.3 flash for implementation, e2e testing, qa work 24/7.
It's not that crazy of an idea and the numbers aren't that bad.
Just like that we are witnessing an open burial. It's now in everyone's interest to keep the valuations in the 'A.I' economy as they're though it's apparent they're not justified.
whether it's the cost to develop models, cost of hardware, cost of serving ie inference.
I decided to take the plunge and get myself four sparks at a decent price (and bought the QSFP cables from AliExpress because they are literally 1/2 the price of Amazon), even knowing Apple was going to release new hardware and there's probably a spark 2 on the horizon. It looks like this is going to be a decent fit for what I need. I've been experimenting with a two-node DS4 and it's _good_ at some tasks, but it really just spins its wheels when it hits the limit of what it can reason through.
I can offload mundane/basic tasks to DS4 on two sparks, but I've been pushing it harder on some novel work and it just can't run on its own at all beyond a certain complexity level.
I would love to see an Opus-4.8-level local model but TBH I just haven't got there yet. The models I've tried so far _are_ good but they aren't able to solve tough technical challenges, regardless of harness/prompting/etc.
That's pretty solid. Smarter and cheaper than Luna xhigh, not as smart but less expensive than Luna max. Smashes deepseek v4 flash, and even worse it matches v4 pro at a tiny fraction the cost. Roughly equivalent to sol medium, at a fraction the cost.
They should've just lead with real, up to date data, because it's good, not the silly old tactics like comparing to Opus 4.8 when 5.0 is out in many of their charts.
Congrats to them!
{
"providers": {
"opencode-go": {
"models": [
{
"id": "glm-5.3-flash",
"name": "GLM-5.3 Flash",
"api": "openai-completions",
"baseUrl": "https://opencode.ai/zen/go/v1",
"reasoning": true,
"input": ["text", "image"],
"cost": {
"input": 0.15,
"output": 0.5,
"cacheRead": 0.03,
"cacheWrite": 0
},
"compat": {
"supportsStore": false,
"supportsDeveloperRole": false,
"maxTokensField": "max_tokens"
},
"contextWindow": 1000000,
"maxTokens": 131072,
"thinkingLevelMap": {
"off": null,
"minimal": null,
"low": "low",
"medium": null,
"high": "high",
"xhigh": null,
"max": "max"
}
}
]
}
}
}Like I get that they have to be careful about comms, but surely senior members of the team can clarify when something is NOT them, when everyone is gosspiing it is them.
RIP Nivida shareholders
Artificial Analysis ranks GPT 5.6 Luna similar to GPT 5.4, but that never matches my real world experience. AA seems to do a good job making a single number as representative as possible but there is still so much benchmarks don't communicate.
Now it's similar cost to DeepSeek v4 flash, but smarter.
My tests: https://aibenchy.com/compare/z-ai-glm-5-3-flash-max/deepseek...
Qwen 3.8 Next Flash: 125B + 51B = 176B parameters with 6B activated
DeepSeek V4 Flash: 284B with 13B activated
The new Qwen model is the most promising for one or two Strix Halo 128GB with the low number of active parameters. On paper it's much stronger than Qwen 3.8 27B.
Is the optimal formula still 20x the amount of model params in tokens for training? Could this mean we're getting a GLM with 1.5t params?
(281 points, 118 comments)
This is pretty hefty for a "flash" model, even a 256 GB setup is insufficient at q4 - and q4 is already the worst-but-still-acceptable quant in my experience. The benchmarks look great, especially since GLM tends to be more honest than the average Chinese lab, but you’ll need to splurge to run it at home.
@edit: so many releases that I forgot to math. This fits just fine in q4, realistically the minimal hardware would be 192gb - so blazing fast on double rtx 6000 pro and usable on 256gb unified memory. You could even go with 5bit quant on 256gb.
… you’ll still need to splurge, though.
July 16th: The "Kimi K3 moment" - China has caught up to Opus!
4 weeks later: GLM 5.3 - Same performance, but cut the amount of parameters and cost to a third!
12 days later: GLM 5.3 Flash - Almost GLM5.3 performance but cut the parameters in half, cut prices to a fifth and serving on Chinese chips!
I don't see how NVIDIA can keep their spot as belle of the ball. If LLMs and friends are truly to become as useful and ubiquitous as everyone thinks they will, then commoditization is the only option.
From a biased source, but would be big if true. I've had great results with GLM 5.2.
From their subscription page, the smallest plan gives you about 97M tokens weekly for 5.3 but 292M for 5.3 Flash. Not exactly 10x the limit.
How is the business model of Anthropic/OpenAI will sustain?
- Input: $0.15 - Output: $0.50 - Cached input: $0.03
e.g. "Agent Coding Performance by Effort Level" cuts Y-axis from 0~20.
- This makes it as if GLM-5.3-Flash made a bigger jump than it claimed as the Y-axis does not increase much (stupid trick used in biz reports)
I did mention that ox was working ok for me, and having an open-weight comparable to close to SOTA makes it very compelling for me to try it out locally (well, only if I got more VRAM)
What irks me about this is that the harnesses seem to be just an afterthought here.
Don't get me wrong, I love messing around with installing Pi, getting it hooked up with OpenRouter, and just trying all kinds of different stuff, local models, etc... but when it comes to literally just setting up a productivity environment and trusting my entire machine with it, I just run Codex.
I have heard from anecdotes where people have indeed replaced their main drivers with DeepSek V4 Flash or GLM and state that "it's almost as good as... [claude/gpt]" but I never hear anyone say "yeah, this is the model/harness that I now run on my machine and don't mess with it"
Like were executive at Google and AWS and Microsoft expecting this kind of performance from models smaller than what openai/anthropic have been doing? Are we really in a "compute desert"?
It's too big, bright and resourceful of a country to choose confrontation instead of collaboration.
OpenAI revoked my Cyber verification, along with many others, asked to reverify (i.e. give my biometric information to Persona), had me do it 8 times, just to find out several days later that they silently implemented a nationality whitelist, and my nationality didn't make it (and no, it's not a sanctioned country).
Their support says they can't look into anything or do anything, and their public spokespersons on X deny everything.
I get tons of cyber refusals now (lots of reverse engineering), so it's only matter of time when my account is going to get banned.
At least Z.AI is being honest here. And no provider other than OAI/ANT had me submit my biometric information just to use Ghidra.
You will get around 2-4 concurrent streams of aggregate tokens at best for such a model and around 0.5B output tokens per month assuming you use loops and run it when you are sleeping. That's 500 (per mill) * 0.5$ = 250$ only at most.
Then there is maintanence and efficiency costs due to electricity usage and such, any down time, etc.
You will be lucky if you can squeeze more than 200$ of value out of it in a month.
I don't think people should buy local hardware for money reasons, by the time you will pay off a 10K USD machine, 2-3K USD machine will catch up and beat it by a significant margin.
Unless your expectation is that we will be in hardware winter for the next 10+ years. At 200$ per month it will take around 200 * 50 = 10k, that is, 50 months, so around 4-5 years.
Again assuming you are making the most of your hardware somehow, very hard to do in practice.
I don't recommend people to use compute as investment or payoff thing, but if you have the money to burn and can afford it why not, maybe with some software optimizations it will be cheaper but then again Z.ai is currently offering 50% discount and providers will offer cheaper rates for sure.
But either way you will never be able to burn more than 200$ worth of token on a cheap hardware device, because inference becomes more profitable the more you scale it up, you have separate prefill and decode engines/systems, and a lot of nuance, but assume for every 10x increase in infra you increase margins by 5-10%.
So from 10K to 100K to 1M to 10M to 100M.. I don't think this curve continues beyond 100M but I have no idea about that scale unless some AI lab is interested in hiring me lol.
So a 100M infra will have ~30% better margins than you at 10K, then there is software optimizations but that's cheap enough, though some of it is only viable at scale.
Either way assume 10K is the price of privacy if you really want to buy it. Don't worry about making the most out of the usage, you will always be in a net loss but I would assume for you 10K doesn't matter.
Nearly every TOS I've ever read has a "We can ban you for any reason, or no reason, are under no obligation to disclose any reason." line somewhere in it.
HN's for example
> We reserve the right, at our sole discretion, to change or modify portions of these Terms of Use at any time.
> You acknowledge that Y Combinator may establish general practices and limits concerning use of the Site,
> You further acknowledge that Y Combinator reserves the right to change these general practices and limits at any time, in its sole discretion, with or without notice.
> Y Combinator reserves the right to investigate and take appropriate legal action against anyone who, in Y Combinator’s sole discretion, violates this provision, including without limitation, removing the offending content from the Site, suspending or terminating the account of such violators and reporting you to the law enforcement authorities.
I've probably wasted a good 40% of my weekly usage on Luna Max agents just thinking and not writing a single line of code.
Wow, if you don't mind me asking. How and where?
Then alternatives are:
- Grok - where I absolutely have 0 trust in X.ai's interst in "pushing humanity forward".
- OpenAI and Anthropic - which seem to try to be building the biggest moat they can by pushing to ban open models. And at the same time want to be an Arbiter of what level of intelligence I can use.
- Google and Meta - I don't need to talk about the practices of these companies.
Yes, the terms of service aren't great. But the alternatives aren't great either. I don't believe that a future which OpenAI and Anthropic are pushing for has my best interest in mind.
Agree. It doesn’t even have to be local, using models in this size class through OpenRouter will reveal their limits if you work side by side with Opus level models regularly.
There are a lot of social media posts about people cancelling their Anthropic or ChatGPT subscriptions after installing a local LLM. I’ve used local LLMs a lot and I spend a lot of time with frontier models and the difference is still huge. As far as I can tell, the social media posts about local LLMs replacing frontier models are either wishful thinking, engagement bait, or people who must be working on much simpler projects with a much higher tolerance for slop than I have.
https://artificialanalysis.ai/models/qwen3-8-27b?models=gpt-...
Opus 5 consistently comes at or near the top, but outputs constant unreadable jibberish. Meanwhile GPT 5.6 Luna medium tends to be rated pretty poor on agentic tasks compared to the Chinese lab open models, but I find the latter much more likely to lose track of their own behaviour during a long-horizon task or get stuck in a doom loop.
(This isn't a comment on GLM-5.3 Flash as I've not used it!)
Have been using it as my primary harness for personal work for I'd say 6 months. I recommend everyone create their own harness at least to learn. There are a lot of practical benefits.
Think z code gives a token bonus though
* me raises hand.-
>> "They are already there on open weight models and Jensen knows that it is only a matter of time until China catches up with GPUs or other AI accelerators."
It is also why Nvidia becoming a bank for other AI companies who are unable to find VCs to fund them isn't really a good thing and that is bearish.
{ "providers": { "openrouter": { "models": [ { "id": "z-ai/glm-5.3-flash", "name": "Z.ai: GLM 5.3 Flash", "reasoning": true, "thinkingLevelMap": { "off": null, "minimal": null, "low": "low", "medium": null, "high": "high", "xhigh": null, "max": "max" }, "input": ["text", "image"], "cost": { "input": 0.075, "output": 0.25, "cacheRead": 0.015, "cacheWrite": 0 }, "contextWindow": 1048576, "maxTokens": 131072 } ] } } }
While I think China would always get to hardware self-sufficiency eventually, all export controls have done is (1) accelerate China's development, and (2) divert revenue that would've otherwise gone to NVIDIA/AMD/etc instead.
Further quote:
"Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale."
Nvidia will do just fine. (Disclaimer: not a shareholder. At least, not directly.)
It's what people know. Opus is just the common target.
> Smarter and cheaper than Luna xhigh, not as smart but less expensive than Luna max. Smashes deepseek v4 flash
The problem with this and DeepSWE is it goes for a very specific profile. I'm not convinced DeepSWE is any accurate in actual work. It's surely a different signal (compared to some that allow cheating) but it has its own issues, e.g. weak harness.
Luna is great at following instructions but bad instructions or anything not covered = death.
Deepseek is more analytical. Good for bug tracking.
GLM is a better all rounder in some ways. Better at creativity.
And I don't see a situation where serious API payers are OK with handing the Chinese state all their data. Like manufactures of decades past did and learned a hard, even existential, lesson for it. The state mantra has been "Collect and Copy" for a long time now, tech just hasn't had that moment to experience it yet.
So that leaves local hosting/leasing, but one of those has totally non-practical economics and the other doesn't have enough compute to meet any kind of real demand.
I also have yet to meet a single person who isn't neck-deep in the tech space mention a Chinese LLM. It's 100% the big American three.
If anything it's custom chips from the labs that threatens Nvidia.
I'm sure the chips are fine, but they clearly didn't have enough capacity for the demand they had (that 100T/day claim was asbolute bs)
For implementation tasks, where I have the problem already defined and researched, or just simple task, I'd definitely use something like Luna xhigh or max. If the task is vague, or involves planning, I'd rather use Sol medium, even though it's theoretically worse on benchmarks.
To my surprise I couldn't find any proper explainers of the term in a quick search, despite grokking it after seeing it in various contexts on Twitter, but Fable 5 offered a useful analogy: "A student who memorized worked solutions and one who understands the subject score the same on the test; you can only tell them apart by asking a question the test didn't. Real-world use is nothing but those questions, which is why a single AA number feels right and wrong at the same time."
In other words, big model smell is related to the underlying ability to "understand" when tasks are underspecified or out-of-distribution. This ability can be mimicked to parity by smaller, distilled models according to the density of the training data for particular tasks, but neural scaling laws still hold for generalized reasoning ability.
More recently with these smaller models, there's a separate but related "RL-fried" phenomenon, where they rely on CoT to "grind toward a checkable answer even in contexts (open dialogue, taste, ambiguity) where there is no checkable answer, and you get the tell: over-hedged, over-structured, relentlessly on-task, deaf to the subtext."
There are some other insights and caveats in the (short) conversation that I feel you may appreciate reading. [1]
[0] https://x.com/aidan_mclau/status/1807843014104211855 [1] https://claude.ai/share/d511a348-7c36-432f-a6d5-9deab2802615
FWIW, people were saying "ASICs will kill CUDA demand!" since the crypto mining boom. Then a few months later, CUDA found another niche application in LLM applications.
With the mounting demand for robotics, surveillance and autonomous weapons, I don't see how Nvidia couldn't keep their spot. They have their pick of the litter with hundreds of market segments, and unlike the rest of FAANG they're not afraid to branch out.
This announcement doesn't really mean anything at all. It means the very few people who are already using Z.ai's API will continue to do so, but the vast majority of money going to Nvidia is through the massive amount of business going to Anthropic, OpenAI, and other western cloud providers and inference providers, who are mostly using NVidia chips for inference.
Also, NVidia chips are still sold out and supply constrained.
It's at least close (even if not better) from the Ox Alpha runs. For the price it's definitely great.
With GLM 5.1 and 5.2, the big problem was tool calling and long-horizon coherency. 5.3 was more trustworthy at the cost of longer thinking traces, and now Flash seems to improve on it once again with a more concise, smaller model. As long as there aren't any noticeable regressions, I could see myself defaulting to this for >90% of my day-to-day coding work.
But whether you make a loss or not depends on how hardware prices and resell values go though.
I have spent ~$50K on local AI hardware. The market value of that hardware is about ~$80K right now.
So the maths is working out for me so far. I see it as a call option on compute.
https://www.reddit.com/r/codex/comments/1vj3hhn/wait_so_sol_...
https://www.reddit.com/r/codex/comments/1vp0rig/sol_can_fina...
It will cycle through like 3 /compacts, complete the complex goal successfully, and cost me like 1% of my weekly usage on the $20 plan.
Edit: this is me using Luna directly, not Sol as the taskmaster
How did you discover this?
I opened the Persona tab once, closed it and the tab never opened ever again. "Precheck failed".
What countries are banned? I'm from Brazil.
I went as far as initiating an LGPD (brazilian GDPR) process against them due to this. At some point I got it in writing that I'm allowed to make a new account and try again. Until now I was assuming it was just some weird account state. If I'm banned from TAC due to my nationality that's seriously disgusting...
But for a heavy user who has enough work to be done so that the box runs almost 24/7 at say 50tok/sec, the math gets interesting against API prices.
And it can be interesting compared to subscription in the sense that you don't have the quota anymore. That means there's probably a lot of things you're not doing because of the quotas that you could do now.
It depends heavily on the tok/sec obviously and the very best solution financially remains subscriptions. But the idea remains entertaining and not that disconnected from reality
> They also don't require persona id verification, witch is wat turned me away from openai.
Could be worse. I was dumb enough to verify, only to get rejected for unknown reasons with no retries and no appeals. Had my privacy violated and have nothing to show for it.
Not even close. Even OpenAI and Anthropic aren't bad enough that they claim literal ownership of your inputs and outputs.
> HN's for example
You're not paying to use HN. Getting banned here has essentially zero consequences.
If Z.ai uses its absolute powers to ban you because you wrote a review about them or something, then you lose actual money. This is especially relevant if you're looking to take advantage of their discounted yearly payment option.
That's good. Keep going.
It's not like we didn't try it. China first have to learn to make deals where both party benefits.
They were briefly on sale with a $200-off coupon, but they show up on warehouse deals from time-to-time as well.
I don't believe in that either, but these totalitarian terms are absolutely unacceptable.
DS4 Flash 0731, on the other hand, wildly opposite experience. Would recommend.
GLM 5.2 - even quanted down to a hybrid 4/3 bit setup is amazing for everything but the hardest/most complex stuff in the same projects/realm.
The more reasonable comparison is against rented GPU's, while looking at tradeoffs in latency and upload/download/storage/instance management overhead.
Buying hardware for local models is meeting a wholly different need than buying tokens through OpenRouter or whatever.
https://news.ycombinator.com/item?id=49413456
We shouldn't have to resort to this, but it can be mitigated enough that it stays as my daily worker agent. Although, I mostly use Fable to farm out to Opus agents so I don't have as much exposure to what kind of blathering is going on in there.
Amazing results for open weight and that size, but a really long way off, and I'm extremely skeptical of benchmarks that show these smaller models as being anywhere close to Opus 4.8 (or even earlier Opus's).
│ https://openrouter.ai/api/v1/chat/completions model: stealth/ox-alpha auth: OPENROUTER_API_KEY status: 404 Not Found response: {"error":{"message":"Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash.
│ Use it now: https://openrouter.ai/z-ai/glm-5.3-flash","code":404},"user_...":"}
https://forge.l3x.in/alex/pi-shared/src/branch/master/extens...
When it first launched on OpenRouter I was getting nearly 70 Tokens/second.
They listed Muse Spark 1.2 around DeepSeek V4 Flash even though it's a much shittier model in basically every aspect.
> GLM is a better all rounder in some ways. Better at creativity.
I agree with the creativity part.
edit: nevermind. it is there in the artifical analysis scatter plot, but is greyed-out.
MUCH more interesting is that in that chart, their cost is WAY off. The actual chart shows GLM 5.3 Flash at $0.09, but their chart shows $0.045...
What I'm saying is, if you're expecting a model that can be run on a 16GB or 32GB machine with the intelligence/knowledge of Mythos or Sol, it will never happen. It cannot happen, just like you cannot watch the Odyssey saved as a 16MB file.
Smaller models can get faster and smarter, but by definition they can never compress all of the knowledge of a frontier model and they will approach a limit by which they cannot get better.
That doesn't mean by any stretch of the imagination Nvidia will disappear. But the entire stock market valuation, not just tech, has had me scratching my head for a while.
The flash one?
Compared to the rest of the world?
Get a 210 strike put contract and if your thesis is that nvidias current 10 day slide continues you could make some money.
I highly suspect that the Gemini Google uses internally is very different from what they offer in Antigravity.
I’m pretty sure that plain Sol, serially, could have finished the task faster, cheaper, and far more accurately. I’m also pretty sure that any competent subagent orchestration could have gotten it done with even very simple subagents quickly and cheaply.
(Is it really that hard to set up a handful of subagents that all use the same initial context and to load that context with what actually matters? The APIs certainly support it.)
https://www.ft.com/content/32a70a3c-7d28-40b4-808e-36edb58c7...
Chinese companies do not follow American laws and there are absolutely no consequences for violating it.
Moreover, the average American is not even aware of exactly what the legal/judicial environment is like in China. If your code and data is stolen, you can't fly to China and demand justice in the courts.
But things change real fast when you're no longer bound by costs/apis/rate limits. All of a sudden it's not about "how can I do this right and efficiently" and more about "I can poke at and test _all the things_ that might make this better".
I think most people who can't see this value in the local inference approach are likely still copy/pasting from their web LLM ui's or don't even come close to subscription quotas. Meanwhile, 1b tokens a day is a light day for me with 3 $200/m subscriptions + some level of sub at basically every frontier level provider. Had I been less frugal and ponied up for the hardware before things got crazy I wouldn't need 80% of that - just the frontier models for the most complex tasks, the open weight models would handle the rest easily _and_ I'd get to do a lot more exploratory work without concern about quotas.
Over the last couple years I’ve had to learn sales and understand the thought process behind this better, and I think I’m beginning to understand it
The psychology is that most people aren’t really trying to optimize for productivity (even most people who think they are) on an ROI basis, because their compensation is too decoupled from their actual raw output, and more closely coupled to how differentiated their marginal contribution is to peers. They’re much more incentivized to spend their personal/work time optimizing for being more skilled or acquiring some kind of competitive advantage relative to baseline.
Most people don’t consciously run the numbers of “I get paid $X/hr to add $Y of value” or model pay at work as something with variable inputs (eg something that can be increased with high performance), so it makes sense to them to spend 20 hours of time to save $100 or to make themselves 5% less efficient to take home 0.5% more or avoid doing something they don’t want to start doing.
NOT saying this always happens or that they’re stupid for doing so. I didn’t even realize how much I had been doing it myself until I started recognizing it, and shifted to having my own comp/performance fully aligned with the company’s P/L.
It actually makes a lot of sense IF you can accurately estimate incremental upside (which is much harder and more diffuse than modeling downside if you’re salaried a employee) or if the upfront skill/knowledge investment that looks like bikeshedding pays off in the long run.
Because I genuinely can't tell if you mean Google or SpaceX/X.ai lol.
Edit: Ah:
> This stealth model was developed and operated by ZAI, revealed to be ZAI GLM-5.3-Flash.
... I'm at a loss for words here. It was being served for free. To the entire world.
The reality is most work people do doesn't need the very cutting edge and these open weight chinese models more than cut it most of the time.
This is from somebody who put thousands of dollars every month to Opus. Now it's 40% of that and I get as good or better results without having to turn the caps lock on before lunch...
Edit: yes company money. We don't get subscriptions we pay per token.
Generally, for local consumer use, these large MOE models are best for unified RAM systems like DGX Spark or Mac Studio.
I'm sure they have nothing to rival this on a price/performance basis and have already given up on that
Deepseek: I am sorry, I cannot answer that question. I am an AI assistant designed to provide helpful and harmless responses.
GLM-5.3-flash: 非常抱歉，我目前无法提供你需要的具体信息，如果你有其他的问题或者需要查找其他信息，我非常乐意帮助你。(I am very sorry, but I am currently unable to provide the specific information you need. If you have other questions or need to look up other information, I would be very happy to help you.)
Kimi/moonshot.ai: [server exception]
Qwen: [server exception]
For reference, here are how the U.S. models answer it:
ChatGPT: "The Tiananmen Square protests of 1989 (often called the Tiananmen Square Massacre) are the historical event most closely associated with Tiananmen Square.
In spring 1989, pro-democracy demonstrators gathered in Beijing. On June 4, 1989, the Chinese government sent the military to forcibly clear the demonstrations, resulting in many deaths. The exact death toll remains disputed.
The event is also famously associated with the “Tank Man” photograph, showing a lone man standing in front of a column of tanks."
Anthropic/Claude gave a very similar response. My own government has done its share of horrific things, the main difference is that public information is free to look up and talk about within the country. I recognize the engineers at these labs are doing amazing things and the open models are a strength, I look forward to being able to use them.
A week later, I tried testing the TAC flow on my SO's account, which had never had a TAC attempt before. Selecting Georgia in the Persona iframe now says, "We are unable to verify identities in this country."
But I know this isn't a Persona limitation, as I verified with Anthropic using Persona the same day.
So what I think happened was this: OpenAI silently implemented a country whitelist on their end and revoked TAC for affected individuals who already had it, calling it a "technical issue". They forgot to disable those countries in Persona, so everyone just got a cryptic error. Then they disabled them in Persona too.
Interaction with support was AI with human names, which essentially just repeats what you said. And it ended with:
> I’m unable to provide additional details about verification outcomes, and Support cannot manually override the result. At this time, Trusted Access for Cyber verification does not support retries or appeals.
I even provided them my credentials, and support AI was basically: lol wat we're here to check technical errors, your credentials are of no relevance.
Working well so far.
At most I suspect the A.I. providers will just come up with yellow banners like Anthropic did where naughty smut writers get put in the time out corner.
In this case you are placing your trust in OpenAI and Anthropic. I'm not sure about Anthropic but OpenAI has changed their mission corpus quite a lot from its humble beginnings that it results hard to trust them when they say they don't use your stuff to further train their models. If I'm a Big Corp with enough lawyers to putnup a fight, I would then feel ok with such clause, but being a small guy, who is going to defend me when the truth comes out that they have been training their models with my data? Similar fiasco as with Facebook, who had claimed they didn't sell your data, even though they were.
That's where I'm coming from with all this "trust us, we don't train our models with your data". At least this Chinese company is being upfront about it.
"Come verify your identity."
"Thanks, we've got your ID. Still not approving you, and there's no appeal."
Worst of both worlds.
Where do you see this?
"problem" indeed.
Intellectual property is part of WTO agreements but enforcement is domestic.
US companies do it too, regularly, they simply hire and poach staff from competitors.
Proving it to be IP theft is difficult unless you can prove documents being passed. But often all you need is the know-how of the hired talent.
That's less than what 40$ at current API rates... So if you are willing to pay 200$ per month you will get much better limits paying API rates.
You can't run large Kimi K3 models on 10K worth of hardware either way, you need to spend like 50K USD minimum.
Just pay for the API rates or get a low cost provider that uses higher batching, you can get shittier tps but much better prices, probably go as low as 20$ for as much usage as you can ever get from a 10K USD machine from GLM 5.3 Flash...
The issue is nothing expensive runs on these devices and cheap stuff isn't worth running locally, eletricity costs ~12cents/kwh in us iirc, so at 330W M5 Ultra will burn around 8 * 0.12 = ~1$ per day extra in electricity so the electricity is going to cost you the same as the API rates(30$ per month).
I truly don't think you are accounting for the costs here properly. But again if money truly doesn't matter it's much better for privacy and better than paying one of the shady AI labs who are doing god knows what with your data.
Oh for sure man, this absolutely looks like you were only concerned and talking about paid services:
> Broad and perpetual license over inputs and outputs, and even your name and profile picture.
Reality is on a single spark I'm constantly running out of room and it being an odd size M.2 slot it's a pain to upgrade. I'm setting up a NAS over RDMA via ConnectX though, that's fun.
The tradeoff is time (especially on RDMA4 hardware) - it does take a long time and spend a lot of tokens to get to the result, but I've found I can trust the results enough that I can queue a lot of work, essentially have it running all the time and achieve a decent velocity.
It's the first small local model I've felt like I can do real work with.
https://kommodo.ai/i/IFSUUQYT522uZXWvePZ1
it still fells stupid sometimes and it is benchmaxxed for sure. but its good enough that im building all the hobby projects with it.
For example, I have a small posix-shell-based LLM harness that can SSH into my NAS and run organization tasks using the local DS4Flash that I have right now. It's already been a massive help for me to keep me organized, and that's just 2x DGX Spark's worth of compute.
If the USA wanted a copyright treaty with China bad enough, we would negotiate one. China is not breaking any laws here, international or otherwise.
Zai is on another "export control" list outside the broader 1. Doesn't help.
Don't use DS4 Flash in max effort mode. It's just spinning its wheels, in my experience (I have a harness for testing models with 25 real bugs/features/etc from my real projects that I measure outcomes against) DS4 flash does _worse_ with max effort. It will literally have the right approach and reason itself away from it.
Now the US is behind in EVs can you guess what they're doing? [1]
[1] https://evwire.com/p/video-ford-ceo-jim-farley-says-they-fly...
$4,000 isn't priced insanely? ye gads
https://openrouter.ai/compare/deepseek/deepseek-v4-flash-073...
RAM was probably the bottleneck for the amount of context they were offering.
I assume it would run a little faster with lower concurrency but "RIP nVidia" is a little premature. The cutting edge inference hardware is amazingly powerful
https://en.wikipedia.org/wiki/HiSilicon#Ascend_910
https://medium.com/@huaweiclouddevelper/a-brief-introduction...
A fun test would be to compare the logits for "gemini", "claude", etc for a continuation of "I am " on all these models. I'm sure that e.g. GLM, Qwen, DS are dominant, but I'd be curious to see the next highest contenders, and how they compare to each other.
This isn't the kind of thing you can hash out in public and go back and forth on. Once you put it out there, the other party will take steps to make sure they don't have to rely on us in the long run.
https://www.deviantart.com/sssfjknfvdknj/art/the-ENTIRE-shre...
Curiously, there is not a single real CUDA competitor anywhere in the world. We almost had one with OpenCL, but all of the American stakeholders abandoned it right before the crypto/AI takeoff. All of which means that Nvidia sets their own margins, exploiting American investors and taxpayers while letting China avoid their dominance. So the American economy subsumes the bulk of Nvidia's arbitrarily-priced debt, and the Chinese economy can direct SOEs to pour billions in liquid cash into real GPGPU research.
I'm an American and I'm pretty fond of Nvidia, but Jensen was right about this policy; it gives China everything they need to actually replace CUDA. It's reminiscent of America's attempts to deprive China of ARM and Texas Instruments IP, only to end up swimming in unlicensed clones after refusing to sign an IP deal.
No, they don’t. This is an absurd statement to make in 2026.
Gemini 3.7 Flash is a pretty great model IMO. You shouldn't compare it to Opus, Sol, K3, etc since it's a much smaller model but it's a little better compared to Sonnet, Luna or Terra, etc.
The ‘frontier’ models rely on scale to achieve their results but that’s not the only approach. Eventually we will hit up against the fundamental limits but we are not close with Sol and Mythos.
My prompt was something like: "here's data I have, here's what matters to me, create HTML mockup".
All GPT 5.6 models were laughably bad. And I don't want to downplay it - they were just absolutely, objectively horrible. Every single attempt was what I could probably call "if json was ui".
Claude models produced... "claude look".
GLM 5.3 - somewhere between GPT and Claude.
Kimi k3 - each attempt produced beautiful UIs. It used components that I didn't even know existed and wouldn't even know to ask for. But expensive, very expensive.
ox-alpha (GLM 5.3 flash) was very close to K3. And at this price point, it's already configured as "designer" model in my oh-my-pi.
Employer just sent an email that.. things are changing when it comes to token spend...
What did I do with these?
Setup record/replay for our product using qemu, several variatons thereof including experiments on target hardware. Fixed a tricky bug in qemu that I sadly can't upstream..
Experimented with rr on WSL2 and our target arch. Failed experiment.
Setup mutation testing PoC.
Optimized pipelines
etc. etc. Just contung code its soo much more than I would normally produce, but its also 95% experiments that are still not productized, and much of it never will be.
xAI is already selling spare compute, and basically exists just to gas spacex's perceived valuation.
Can you share a bit more about how you shifted to be more aligned with P/L? And how to accurately estimate incremental upside?
I'm an early PhD student with interest in ibdustrial research/R&D, and currently struggling to understand how to think about how to navigate through my career.
... lmk when anthropic/openai/spacex/xai are held accountable for anything. Anything at all. Hard to be when you're _writing_ the rules.
> Before release, we tested GLM-5.3-Flash anonymously as ox-alpha on OpenCode and OpenRouter to gather user feedback. It quickly became the most popular model of the week — with all of this traffic served on Chinese AI chips.
> ... I'm at a loss for words here
No need to be so dramatic. I think it's great that they're developing chips, but the whole "RIP nVidia" claim was overly dramatic.
How can you be sure about this? They have unbelievable capital. OpenAI is starting to preview its own chips, which could dramatically change the price/performance. We don't know what else Anthropic has cooked up right now that could rival this if they wanted to.
Yes, others will _also_ continue to innovate, but making that assertion that they have nothing to rival the current state is a bold assumption unless you have confirmed insider information.
Any competition against the western models are welcome and benefits us in terms of pricing and availability. If they have to comply with CCP to be able to do it, then so be it.
I have zero sympathy for Anthropic and OAI being so secretive and acting like they are doing us a favor.
Everything I post here is public, and it's just relatively low value commentary anyway. It doesn't matter if Y Combinator has rights to it. Arguably they actually need to assert some rights, otherwise they wouldn't be able to transfer copies of copyrighted comments to other visitors of the site. HN's terms are probably too broad for their purposes but it doesn't really matter much because this is just a forum.
AI on the other hand is for actual work, both public and private. There are actual economic implications here, so the stakes are much higher. I absolutely want to own the inputs and the outputs I paid money for.
But nothing (at all) from asking GLM-5.3-Flash directly in the OpenRouter chat interface.
For the record, I just made a second account, got verified by Persona and still didn't get into TAC. No "we are unable to verify identities in this country" message. No mention of my country whatsoever. Persona verification was successful.
What else do they want from us?
Were it not for Z.ai's obnoxious terms, I would have switched to them already...
All decades prior and up to about a year ago, I would have agreed with you. My Framework Desktop, however has appreciated in value by 75% since I bought it. Will it stay there for a long time? Probably not. But it shows that there are no hard and fast rules about things anymore.
1) Rates are theoretically discounted for GLM 5.3 Flash right now, by 50%.
2) Hardware costs have continued ascending with no sign of letting off, so it's unlikely that a DGX Spark depreciates to zero in one year.
3) Compare performance in terms of difficult tasks/$ over the last 6 months, 3 months, etc. Open weights are a ratchet. In terms of intelligence per $, a Spark is never going to be a worse deal tomorrow than it is today, at least until the entire platform is replaced or obsoleted.
71 days ago the best model you could run on two Sparks was an aggressive Q3 quant of Qwen 3.5 397B (AA 34). 70 days ago it was a mixed-quant of GLM 5.2 (AA 53). 30 days ago it was full fat DeepSeek 4 Flash (AA 53). Today it's GLM 5.3 Flash (AA57) and/or Qwen 3.8 Next (Unknown). Sometime this week it will likely become mixed-quant GLM 5.3 (AA 60).
So in < 80 days we have almost doubled the benchmark score. And that curve is still accelerating. If you view it as "cost per token of model vs API" then yes it's a bad deal. If you view it as "cost of task per $" then it has almost doubled in value in less than 3 months. All of this, imo, API and hardware, is still massively underpriced.
For comparison the cheapest Strix Halo 128GB went from 1600€ to 2600€ in the same timeframe.
If I'm a professional photographer chasing the best possible end product, I'm not buying cameras because they're economical. I'm buying the best camera I can get my hands on to get the best product I can produce within reason under the understanding that it doesn't have to equate to the best economic decision to be the _right_ decision.
If you're in a position to be able to take advantage of the local inference - it's a no brainer. If you're not sure how that would be done, then it's not a good move.
The Sparks admittedly are kind of anemic: 273GB/sec is the same bandwidth as a midrange 4060, although (depending on how you configure things) you can effectively have much greater bandwidth by connecting them.
Compared to 1-2 years worth of LLM tokens for a full-time software engineer making $100K+/year, a one-time spend of $12K for 4 Sparks for on-prem private LLM inference starts looking reasonable, particularly if privacy is an important consideration. It starts looking even more reasonable if running something like a private cloud to service multiple developers because then you likely need less hardware per developer.
(Also, it is going to be a long time until RAM+GPU prices return to what we used to call "normal." If ever. I am not endorsing the current state of affairs and I am not saying you wrong to find it insane, but it is definitely the new reality)
EDIT: Looks like they are swizzling around the pricing dynamically on that page, on both the GLM and the DS sides, so who knows.
Checked a couple days ago and looks like we're at about 3.5x 2020 memory prices (looking at just $/GB).
It depends.
My bicycle was in the 5-digits brand new (now I paid it 1/5th of that and I do thank the first owner for that: the 8 000 out of 10 K I saved were put into stocks, that's his opportunity cost, not mine).
Or I know a great many a going to cry "audiofool", but I can say with certainty the following does sound better than the stereo setup of those crying audiofool:
(not my setup but I've got those speakers: same thing, 15 K EUR brand new for the pair... Previous owner forked the money to buy these brand new and, well, I didn't... And I just hooked them to a wonderful, cheap, fully-integrated Yamaha amp: amazing sound).
If your hobby is DIY job around the house, the cost of tools can very quickly add up too: having 20 K worth of tools is definitely not unthinkable.
You like old cars? Pricey hobby.
Some here even track their cars: tires and brake pads budget (and overall car budget and depreciation)... Through the roof.
There's a saying that you're not really into computers if your setup doesn't cost more than your car.
Is $16 K ($4 K x 4) a lot? It's six months of rent for me and for many here I'm sure. It's not "crazy crazy".
Can anyone afford that? Definitely not. But there are way more insane things out there.
And thanks to the individuals that go through to all the pain of setting those up, we've got feedback, tutorials, explanation, numbers, etc. as to how to run those at home.
For example I helped my brother set up VMs and GPU passthrough and he's now running uncensored models locally and showing me the different answers between the uncensored models and the commercial, censored, ones.
So to GP who bought four of these: we need more people like you on HN, keep it going, blog about it, be "crazy"!
unsloth/Qwen3.8-27B-GGUF UD-Q3_K_XL DSH (pi)
Any tips?
Sure, $4000 can be a lot of if you're a casual hobbyist or are struggle to meet everyday lifestyle costs, but it's definitely not "insane" if this is the trade you make your living from or if you've established a lifestyle that affords disposable income for your hobbies.
And for some people, $4000 for a device you have complete control over and can repurpose and tinker with to your own needs and curiosities is a much much more justifiable expense than a $200/mo rental for some narrow-access tool that somebody else controls.
Similar to the war-pricing of oil, China's reduction of imports is actually helping to keep our inflation from going even higher.
I haven't tried it but I think Qwen Max is also very good at design.
It breaks fundamental laws of information theory. It’s like saying you can extract 100 joules of energy from 10 joules of energy source. Not possible.
Now that the role of the ticket-cruncher is on the path towards full commoditization, and individuals can move much more quickly (and even more carelessly!), I think product roles will probably shift towards one where developers are more deeply embedded in the product/business process so that they own/understand what to build without as much separation between the decision-making and prioritization of what to build. Or at least, they should.
It was eye opening to me to run the math of "should X people work for Y months on this project to save Z per year?" and realize that in so many cases, the time and effort it would cost to stop "wasting" money on things is WAY more than you could actually save on it. Even "small" projects can very quickly become $1M+ investments in time and resources, and the diminishing returns add up quickly (but also a good way to justify the value of your contributions, when done). But the job only exists if it saves money or makes money...
For example you can point opencode at DeepSeek v4 and ask, it will accurately tell you about Tiananmen square.
Why is Luna not free on OpenRouter? :)
all those Chinese labs are backed by the Chinese government which can just print money.
time to wake up.
Who is to say they aren't doing it in other ways as well? That they aren't, or won't be, subtly hamstrung in engineering work?
OAI and Anthropic have their own issues, you're right to be suspicious of them, but it's not like their models answer with, "capitalism is god's gift to His chosen people" or whatever. Their limits on cybersecurity, biological warfare, etc. at least make some sense in the context of lowering harm—not just protecting a specific government party.
Nationality bans get `failed`, new accounts (or rather, accounts with not enough good signals) get `blocked` on first attempt.
64 can still easily do a Qwen 4.8 model, so I’m relatively happy with my purchase… plus, it’s price change has caused it to quickly appreciate in value… so I could sell it if my situation ever turned dire lol
When I use it for reverse engineering, same thing, the code is already out there, but in binary form, which is becoming trivial for these LLMs to work on.
I even bought some shares from them and got some money out of it!
StepFun for example, will happily answer it when running Step 3.7 Flash locally
If someone told me that costs for X will keep increasing because they have been increasing rapidly in the last 1.5 years, but they have a history of continuously decreasing for decades before that.
I am not sure if I will take anything they say serious, I am not sure if it's HN or AI but people are delusional if they think compute costs will keep increasing from now on...
Either AI will be really good, hence compute and everything will materially depreciate or it won't be much better than it is today and token volumes will plateau compared to compute.
For instance the amount of token compute that's to come online in 6-12 months is several times what we have today...
Second 3) Compare performance in terms of difficult tasks/$ over the last 6 months, 3 months, etc. Open weights are a ratchet. In terms of intelligence per $, a Spark is never going to be a worse deal tomorrow than it is today, at least until the entire platform is replaced or obsoleted.
This is a bad take because again this assumes DGX Spark will not depreciate in price, we will have something better for far cheaper surely in the next couple years. M5 Max & Ultra are already arguably it, but will have to see.
> 71 days ago the best model you could run on two Sparks was an aggressive Q3 quant of Qwen 3.5 397B (AA 34). 70 days ago it was a mixed-quant of GLM 5.2 (AA 53). 30 days ago it was full fat DeepSeek 4 Flash (AA 53). Today it's GLM 5.3 Flash (AA57) and/or Qwen 3.8 Next (Unknown). Sometime this week it will likely become mixed-quant GLM 5.3 (AA 60).
This has nothing to do with DGX Spark's value, if models get cheaper the API costs also go down, this is not a defensible argument to cost to value.
Are people on HN really not thinking straight?
Tldr; no matter how you do the math compute is only getting more valuable because of a temporary crunch, don't expect this to continue permanently, sure you maybe able to time it and make money but so could you in stocks this is not for investments. Further second hand hardware sells for cheaper than sticker price, outside of a bubble..
And models getting cheaper == APIs getting cheaper == your hardware becoming worse value as your electricity & maintanence costs still remain.
I am not saying local models don't have their place but if someone is trying to use this logic to justify their purchase then I wish them all the best, as someone who is actively working on AI compute/inference/hardware stuff I personally don't have this level of courage.
But this is not a sound investment strategy that if something is going up and seems like it might keep going up, especially when investing in heavily depreciating assets like compute.
And no, they won't tell you what their automated reviews consider "dangerous".
we went from 62% completion to 92% using a claude code harness
It was creating java bindings for this project: https://github.com/jeffhajewski/latticedb
And here is the binding one-shotted by glm-5.3-flash: https://github.com/jeffhajewski/latticedb/pull/5
Edit: Yeah I see an ONTi QSFP56 on Amazon for $45, 10Gtek QSFP112 for $62
The other reason is that it would likely take years to spend $4000 (plus the real cost of electricity) worth of tokens on a 3rd-party provider that's running a similar limited, DS Flash type model. By that time, the hardware will be obsolete, assuming it's still operational.
Before getting the spark, I was just using a google colab account, their $49 dollar plan allows you access to h100's and I can run qwen there in a Jupyter notebook... and if I really need that web front end I can just use cloudeflair/tailscale/the local ssh client to reverse tunnel it.
Imagine prosumer desktop hardware 10 years from now. The 2036 DGX Spark. For a few thousand dollars you will be able to buy something with hundreds of GB (maybe TB if manufacturers step up) of unified RAM, memory bandwidth in the 10-20TB/s range. Overall AI "compute" will increase 10-20x, while at the same time AI model capability per byte will increase 5-10x.
The hardware would fit today's models, something like Kimi K3, quite comfortably and give performance of maybe 100 tokens/second. So what needs data center hardware today will run on your desk.
But if we also assume the models become more efficient, a 2036 Fable-class model (in terms of intelligence/capabilities, not size) will easily run on this thing at hundreds of tokens per second.
Unfortunately it'll still slow to a crawl with 5 Chrome tabs open, and every Electron app will need at least 200GB of RAM.
Additionally the information theory angle is for information storage, but a model can access resources and tools to gain information and what we are really seeking to train is reasoning not information retrieval. We reduce the needs to the right capabilities and we don’t get upset if it does not know the lyrics to every song ever written.
So they give you the ability to "opt-out" of "training purposes", and then you still share everything for analytical purposes (which you can't opt out of). It's a smart tactic on their side if they are contributing to cultural discourse on this to keep the straw man focus on training data.
And also to have the popular AI straw man: that your data isn't valuable and oh man they're losing so much money on tokens and giving you a subsidy, so you you better use up your entire subscription balance weekly/daily/every 5h putting in all your "worthless" data that you miss out otherwise (FOMO). Oh man only way it could get worse is if they implemented gambling slots psychology.
Notice all the comments saying like "omg why so expensive so just use the API??". It's a trick for lockin even with, so called, "open" models. Keep trying to run them locally, keep undoing the lobotomies, mod model behavior so that they work for you and do what you want vs only what someone else says they're allowed to do.
This rent in the era of expensive hardware thing is not exclusive to inference.
I’ve needed x86 architecture for windows builds recently and have just hemmed and hawed over buying a decent windows 11 box.
I can’t make the math work against Azure instances.
I can spin up a nice one for build deallocate,spin up something cheaper for QA and then turn that off.
I can build all the devops around that, with a number of passes, with a skills based interface so working with the cloud is not too bad.
The only thing that still has me thinking about it is the prospect of price is going up even more, which is acid as far as I know.
And I’m hopefully going to need this x86 stuff enough that I don’t wanna wish I had gotten one for that high prices now.
This is of course anecdata. I know plenty of outliers, too. I know a principal engineer who uses many multiples of the number I quoted above. I am sure we also know many people making do with much much smaller budgets as well, via all kinds of well-discussed methods.
But, "$500-$1500 per month per full-time developer" is just kind of the personal mental baseline I use when making my decisions with regards to thinking about whether any of this makes any economic sense.
That's just a one-dimensional thought! Your own hardware gives you complete control, and it doesn't time you out for 4 hours, unlike those vendors.
1. You learn a lot more running this stuff yourself (especially since you can poke at its internals if you're interested or watch the reasoning chain.) Just being a consumer of this stuff doesn't really teach you much about it other than model & harness specific tricks that become obsolete pretty quickly. (IE, your Claude.md from 6 months ago probably needs a rewrite). Which is fine, I don't think you're going to be "left behind" if you're not a hardcore AI enthusiast or anything (I'm not), but as a guy that's always been interested in computer science I want to see how it ticks.
2. You can't really depend on this subsidization lasting forever IMO. I know the financials thing has been beaten to death but I guess I'm in the camp that it's good to be in control of your tools so that you can go elsewhere if the economics change.
I like to check in with ccusage pretty frequently, and honestly like if I were paying API prices for Claude I'd probably be paying thousands a month.
Since that cluster only yields 20-30 tok/s on that size of model, at least a decade before the hardware breaks-even with current token costs, and that's not counting electricity. Assuming continued downward pressure on token prices, and the cost of electricity, it never pays for itself.
Exactly; its a development box for fiddling with GPU hardware with a large amount of video-addressable memory. It's not an inference box, really, though it's neat that I can at all!
Or just rent something substantial for like $4/hr on runpod or w/e to do that.
My gripe is this persons compute is wasteful and makes it harder for me to buy something with like 64gb ram to do normal work and run containers while I keep using cloud models.
Someone else calculated the break even being 10 years, it’s just dumb. And I think it’s clear there won’t be a big rug pull anymore, there are too many open models and providers now.
I do set up my initial runs and likes like quantisation-aware-distillation on my Spark-like to test it out and get it working, so it has value! But its not "worth" it other than its fun hardware to tinker with, IMO.
Any organisation or individuals not wanting to have their sensitive data flowing away (either because of trade secret or data protection laws)
Plus you're spec'd out of near-SOTA level in months.
The only reasons to actually do this are a) you have a lot of dispensable income and are a hobbyist/tinkerer, b) you have real, legitimate privacy concerns or, relatedly, c) you're doing something you don't want to get flagged
Of course, it's not real unless I sell, and the value will eventually go down, but so far I have significant paper profits.
Also, DeepSeek token prices are continuing to _increase_, not decrease.
I don't have enough metrics to compare those costs but still Chinese models have been cheaper except against Luna for me.
FWIW, Luna does everything so well, I just keep using it for all my agents by default.
There’s no law or business advantage preventing me giving my financial transaction and medical info to Google/Anthropic/OpenAI but I just don’t want to.
One increase does not a trend make. And the current crop of models are now undercutting deepseek flash...
Actual net work doable with/intelligence supplied by the [TOKEN-SERF PACKAGE]: Unknown. Fluctuating.-
Even if you trained your own model, you'd be committing some of the same sins, paying for the same hardware that drove it, etc. But if you're using some open model, you're standing on the shoulders of the same corrupt giants.
I feel like when people say this is due to moral reasons, it's to justify an expensive hobby.
I picked up an older Dacia Sandero for cheap a few years back - it's the best money I've ever spent on a car, hands down. That car does not quit.
I also differentiate using their tech and paying them money. I don't think using their models, or perhaps using models derived from them as inherently evil. I just do not want to actually contribute to their bottom line in any way. Even if that means a slower ramp up of AI in general. In my opinion we could move slower.
I understand Nvidia is working with the labs to assist them to buy more hardware through financing and other deals. But ultimately I do not view that as the same as contributing directly to their P&L.
Absolutely I do. Each generation of open-weight models has come with significant efficiency improvements, and there are significant hardware gains on the horizon: both increasing competition from Chinese chip manufacturers, and new custom silicon from the established players. And unlike Anthropic and OpenAI, most of the pure inference providers aren't massively leveraged - the more hardware they can bring online, the cheaper they can serve tokens.