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.
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.
(of course if i'm being honest 640kB is fine, i'm sure tons of the world's commerce is handled by less for example, the delta between a system with 640kb of ram and a modern one is near nil for many people, the UX on a PoS terminal does not require more than that for example, the hacker news UX could also be roughly the same)
How refreshing to hear this kind of old-school hacker thinking, in a thread where most people have given up on local computing in exchange for convenience and permanent third-party dependency.
With embedded systems affordable and ubiquitous, hopefully a growing segment of the new generation will also learn to push the limit of available hardware and see how far we can take it. As an engineer there's a satisfaction in solving things with what you got.
There's a new technique, 1-bit family of language models that can achieve up to 9x memory efficiency compared to existing models. Still multiple gigabytes for practical use I imagine, but it's great progress toward local AI, which I believe will be common in the near future. https://prismml.com/news/ternary-bonsai
Thot_experiment is saying that his 2016 Toyota Prius is a great and reliable car for his daily commute and running errands.
Whereas everyone is screeching about its capability gap with a Lockheed Martin F35 lightning.
I'm kidding around. I run 31b models myself too and am perfectly happy with them.
I'm going to switch to local LLMs for most stuff soon.
I stopped doing local stuff for a bit when I realised I didn't know how well it is supposed to work so have been on Claude for a few months now.
I think I'll try OpenCode this time.
Usually I do stuff in devcontainers, qwen code (non local) was the only time I managed to lose some work as it got confused when I ran out of tokens.
There's still quite a way to go - it does seem like Claude code itself is pretty badly coded, so I think there is a space for open source to come in with a high quality harness at some point.
If you believe what you read here, the gap is closing fast.
> For those of us a bit crazy, we are running KimiK2.6, GLM5.1
Yes, those can compare to Opus, but you can't run those unquantized for less than $400k in hardware.
A single M3 maxed can run a Q2 Kimi 2.6, though thats with a hardly degraded perplexity.
2x M3s with RDMA can run a lossless Kimi2.6 at Q4, but with CPU only you would get okayish decode but horrible (+1m) TTFT, that wouldnt be a great _interactive_ experience.
Doubtful. The increase in demand is greatly outpacing supply, and all signs point to a continued acceleration in demand
> If I could drop $10,000 to have an effectively permanent opus 4.7 subscription today, I would.
lol well obviously, but realistically that price point is going to be closer to $100k, with a perpetual $1k a month in power costs.
If we think about the near future, something like Kimi2.6 is within the realm of Opus 4.6 today, but requires closer to $700k in hardware to run.
I predict the B200 data centers we're build today will be obsolete in 3 years and we'll be using whatever models and hardware that isn't even on a road map today. Likely not NVIDIA, likely not OpenAI or Anthropic. Maybe Chinese?
In the mean time, we must continue building software with the clumsy coding agents tied to cloud services as this (for now) seems to be about the only area where AI economically makes sense.
The best analogy is the difference between having N senior level engineers working for you, versus having N entry level engineers.
With frontier cloud models, you can give a single invocation one task, and it can figure everything out.
With local models, you have to manage the inputs and outputs quite a bit more, but you can achieve similar results for tasks you set up harnesses for. They are not as a good at finding the right answer internally from their own weights, but they are very capable of ingesting context and reformatting text - for example, for debugging, local models can debug issues quite well if you give them the error and documentation for a particular feature you are trying to implement.
Fixed that for you. Right now most models produced are based on floating point maths and probabilities, which is "expensive" to do math on.
Microsoft has researched 1-bit LLMs which can run much more efficiently, and on much cheaper hardware[1].
If this research is reproducable and reusable outside their research models, this means the cost of running self-hosted LLMs will be reduced by an order of magnitude once this hits mainstream.
For niche applications, sure. For general use, I think the tendency towards the best model being used for everything will–to the model publishers' delight–continue. It's just much easier to get a feel for Opus and then do everything with it, versus switch back and forth and keep track of how Haiku came up with novel ways to dumbfuck this Sunday evening.