Fine tuning these models (at least with PPO or equivalent) requires even more VRAM than inference does, potentially 2-3 times more.

I also wonder about JS only, Python only, etc models.

Maybe the future is a selection of local, specific stack trained models?

Nice.

What did you use to do this, something standard like llamacpp or something else like vllm or your own contraption ?

The big difference will be measuring seconds per token instead of tokens per second.

I'm not running local for exactly the same reason, to not stress my components. As it seems we are in for a long haul due to this AI bubble (can't wait for it to pop) so need to make sure I survive this madness, as for sure I can't afford to replace anything right now.

Do you encounter looping issues at such low quants? How do you deal with those?

Benchmarks only give you the roughest idea of how models compare in real world use. They're essentially useless beyond maybe classifying models into a few buckets. The only way you gain an understanding of something as complex as how an LLM integrates with your workflow is by doing it and measuring across many trials. I've been running Opus 4.7 in Claude Code and Gemma 4 31b in parallel on projects for hours a day this past week, Opus 4.7 is definitely better, but for many things they are roughly equivalent, there are some things on the edge that are just up to chance, and either model may stumble across the solution, and there are some areas of my work that reliably trip up both models and I get better mileage out of writing code the old fashioned way. I understand that I'm just one data point, but I'm not writing CRUD apps here, I'm doing DSPs and weird color math in shaders, I don't think any of it is hard, and the stuff that I think is hard none of the models are good at yet, but idk, they just don't seem that extremely disparate from one another.

FWIW I think Gemma 4 31b is more likely to be of use to me than Sonnet, idfk, maybe it's a skill issue but I love Opus 4.7, undisputed king, but Sonnet seems borderline useless and I basically think of it as on the same level as Qwen 35b MoE.

I’m building a pipeline and testing against gemma4 and Gemini’s 3-1 flash. Both are very good on certain tasks and even n-way clustering works almost perfect almost always.

But they diverge greatly on other particular ones whenever the ViT tower and the apriori knowledge of the world is crucial. I wish Gemma was on par but both me and Google know they not.

You do need to ask whether or not Sonnet or Opus are overkill for a lot of work though. If Gemma4 with some human effort can achieve the same result as Sonnet then it's arguably a lot more cost effective as you're paying for the person to operate each one regardless.

False. The absolute capability is irrelevant, with the proper harness 31b is more than adequate for a very large portion of the tasks I ask AI to do. The metric isn't how good the model is at Erdos Problems, it's how reliably it can remove drudgery in my life. It just autonomously reverse engineered a bluetooth protocol with minimal intervention, it's ability to react to data and ground itself is constantly impressive to me. I do a ton of testing with these models, today I had Gemma answer a physics problem that Opus 4.7 gave up on. With a decent harness and context the set of tasks where their capabilities are both good enough is very surprising. The tasks I have that stump Gemma often also stump Opus 4.7.

It would be true, if model providers did not throttle their models. I do not have definitive proof they do but the rumors are abundant.

No, exactly the opposite actually. Qwen3.6 is too imprecise for long running agentic tasks. It doesn't have the same ability to check itself as Gemma does in my testing. I keep Qwen MoE in vram by default because there are tons of tasks i trust it to oneshot and it's 90tok/sec is unparalleled, anything where I don't want to have to intervene too much it can't be trusted.

Cool, thanks for the information. I guess they drive prices down by massively parallelizing requests on say an H100 X8 array? So this is spread across. So if I say, wanted to use it for 8 hours a day in my theoretical world it’d be too expensive. My work definitely wouldn’t pay $100,000 for a server farm even if it’d give an AI to all our employees, you’d have to have engineers, a colocation space, basically all the problems that companies didn’t like and went to AWS for.

Why? These models are going to keep drastically improving and given all the new data centers token prices will probably drop a lot in the future. Seems shortsighted given the absurd timelines these things have been improving on.

taalas!!!

Two Mac Studio M3 Ultra 512GB and 1 USB cable can run all those models - maybe about $30,000 in hardware - and based on my benchmarks, those Mac Studios were twice as fast as the A100s on Deepseek v4 Flash, which has a quantization but not really a lossy one.

Not by much, and moving goalposts makes for a bad comparison. Local open weight models are already more powerful than frontier models from only a year back.

If you believe what you read here, the gap is closing fast.

For most of that time, I don't think many people had much use for more ram than that. If demand picks up, companies will provide it.

And the Mac Studio was available with 512GB until ram got scarce and they cut the max in half recently.

What is everyone running DeepSeek v4 Flash with?!

It’s currently unsupported on Llama.cpp and vllm doesn’t support GPU+CPU MoE, so unless all of you have an array of DGX Sparks in your bedroom, what’s the secret sauce?!

Just because you read it on a github repo doesn't make it true, it also doesn't take into account cpu temps and inevitable throttling you'll encounter.

The amounts of API tokens many large companies are using through, say AWS bedrock are quite high. We've seen leaks on the bills for real world use cases. It's not unreasonable to see normal individual subscriptions as possibly subsidized.... but do we think someone like Anthropic is going to be subsidizing 7, 8, or even 9 figures monthly bills from megacorps? Because said megacorps will swap out to a competitor immediately, so your subsidy is unlikely to lead to loyalty or anything.

If Anthropic and OpenAI are subsidizing the metered API usage, their model is going to end up just as successful as MoviePass. They are burning enough money on the training costs already.

Anthropic and OpenAI make money on API calls, margins have been reported in public filings. Subs are subsidized.

> I don't think cloud models are going away; the hardware for good perf is expensive

I think local AI will win in its niche by repurposing users' existing hardware, especially as cloud hardware itself gets increasingly bottlenecked in all sorts of ways and the price of cloud tokens rises. You don't have to care about "bad" performance when you've got dedicated hardware that runs your workloads 24/7. Time-critical work that also requires the latest and greatest model can stay on the cloud, but a vast amount of AI work just isn't that critical.

> higher param count models will remain smarter for a looong time

They're not smarter, they just know more stuff.

You probably don't need knowledge about Pokemon or the Diamond Sutra in your enterprise coding LLM.

The "smarts" comes from post-training, especially around tool use.

If 8 x RTX 6000 is getting you 20s before initial token, how are cloud vendors doing this?

You're right, I was thinking of Qwen. K2.6 will run at UD-Q2_K_XL precision on 4x RTX6000 boards, but I have no idea if it's worthwhile.

It's old rack mounts. Only one of them has some ECC DDR4 worth something.

The architectural problems I deal with day in day out leave no room for atrophy. This is just cope.

> I consider myself a computer person and I dont think I even own $4000 of computer hardware in total

A top-spec MacBook Pro is >$4k, so I assure you that plenty of computer people do own $4k of computer hardware.

Hell, most tech folks are wandering around with a ~$1k smartphone in their pocket too.

Fwiw you can finance a car over something like 7 years now. So a lot of people will be paying like $750 per month, not $50k lump sum.

Plenty of gamers own serious GPU rigs that are reusable (at least to some extent) for local AI inference. That's almost certainly more than 0.1% of the populatiom.

I guess I wasn't clear-- I wasn't so much making the point people do own $4000 in GPUs (though I suspect you are massively underestimating the number who do, also before the current market conditions this would have been more like $2500 in gpus...), but they certainly could per the evidence of car ownership.

A car is super useful, so is an AI. But even if we decide cars are incomparably more useful a great many people pay much more than $4000 over the minimum viable car, and that's money that could be deployed to secure access to private, secure, and autonomous AI facilities. A few thousand dollars in computing is consumer hardware, or at least could easily be with more reason and awareness driving adoption.

People spend a LOT of money in things less useful than local copy of qwen3.6-27b can be.

Probably you're focused on coding agents? I bet someone could use that kind of hardware to filter snarky comments

Here is an example-- I'm running hermes + qwen3.6-27b on a workstation GPU (an older RTX A6000 which gets 55tok/s, though people run this model on more limited hardware).

A friend an I had previously worked on an entropy extraction scheme and he recently got around to making a writeup about our work: https://wuille.net/posts/binomial-randomness-extractors/

I instructed the agent to read the URL, implement the technique in C++ for 32-bit registers, then make a SIMD version that interleaves several extractors in parallel for better performance. It implemented it (not hard since there was an implementation there that it read), then wrote more extensive tests. Then it vectorized it. It got confused a few times during debugging because the algorithm uses some number theory tricks so that overflows of intermediate products don't matter and it was obviously trained a lot on ordinary code were such overflows are usually fatal. I instructed it to comment the code explaining why the overflows are fine and had it continue which mostly solved its confusion.

It successfully got the initial 12MB/s scalar implementation to about 48MB/s. Then I told it to keep optimizing until it reaches 100MB/s. I came back the next day and it had stopped after 6 hours when it achieved just over 100MB/s. Reading what it did: it went off looking at disassembly, figured out what hardware it was running on, and reading microarch timing tables online and made some better decisions, tried a lot of things that didn't work, etc. (And of course, the implementation is correct).

I'm pretty skeptical about AI and borderline hateful of many people who (ab)use it and are deluded by it-- but I think this experience shows that a small local model can be objectively useful.

(oh and this experience was also while I only had the model running at 19tok/s)

Running the model in a loop where it can get feedback from actually testing stuff allows you to make progress in spite of making many mistakes.

I could have done this work myself but I didn't have to and I certainly spent less time checking in and prodding it than it would have taken me to do it. In my case I wondered how much faster parallel extractors using SIMD might be-- an idle curiosity that would have gone unanswered if not for the AI.