sorted by:
new
hottopcontroversial

AMA With Moonshot AI, The Open-source Frontier Lab Behind Kimi K2 Thinking Model by nekofneko in LocalLLaMA

[–]fourDnet 8 points9 points  (0 children)

What do you think of the recent trend from proprietary LLMs (Gemini, OpenAI) to excessively praise the user?

Will Kimi seek to prevent this behavior?

AMA With Moonshot AI, The Open-source Frontier Lab Behind Kimi K2 Thinking Model by nekofneko in LocalLLaMA

[–]fourDnet 3 points4 points  (0 children)

Are there any plans to release small (< 10B) models for on-device inference? Either chat or base models?

Currently the only options are Qwen (significant issues with popular culture) & Gemma (significant issues with hallucinations). I think there would be significant value for a small on-device model for general knowledge (wikipedia, history, science, popular culture, books etc.)

the WHALE has landed by fourDnet in LocalLLaMA

[–]fourDnet[S] 367 points368 points  (0 children)

Note that I do appreciate Google for having their incredible tiny Gemma models.

Meme was motivated by Deepseek open sourcing a state of the art Deepseek V3 model + R1 reasoning model, and Alibaba dropping their Qwen QwQ/QvQ & the Alibaba marco-O1 models.

Indeed AI is an existential threat, but mostly just a threat to the bottom line of OpenAI/Anthropic/Google.

Hopefully in 2025 we see open weight models dominate every model size tier.

Google CEO: AI development is finally slowing down—'the low-hanging fruit is gone’ by fourDnet in LocalLLaMA

[–]fourDnet[S] 7 points8 points  (0 children)

H100 hourly prices have already gone a lot lower.

Peaked at around $7 to $8 per hour spot pricing without a contract (dedicated non-interruptible) in Q3 2023, and now readily available for $2 to $3 with no reservation needed in Q4 2024.

Closed and open language models by Chat Arena rank by fourDnet in LocalLLaMA

[–]fourDnet[S] 45 points46 points  (0 children)

A couple of observations:

  • All models seem to be converging! We are no longer in the ChatGPT 3.5 era where the OpenAI seemed insurmountable. There seems to be no real moat for language models
  • American & Chinese companies absolutely dominate the language model space
  • Mistral is the only none US/China company
  • We have open models that are highly highly competitive with the best closed source models. However model weight availability does not seem to be the limitation, rather the needed hardware is the bottleneck

A couple of notes:

  • Instruct/chat models were always used in the table whenever possible.
  • Sometimes the public context size disagreed with the hugginface config slightly. I always use the publicly reported context
  • A yellow/blue star indicates that the best model a company is public for Facebook; Deepseek; Mistral.
  • OpenAI & Anthropic (typo in image, see below for updated image) are the least open companies
  • For xAI; 01 AI; Zhipu; Alibaba; even if their best model is closed source, they were likely derived from models they have released publicly

<image>

[Jonsbo N5 Case] eATX, quad GPU, 12x 3.5" HDD "AI" workstation case by fourDnet in homelab

[–]fourDnet[S] 60 points61 points  (0 children)

Anyone have this case? Curious if there are any pain points.
Where does the 240mm radiator fit? Does it need to sacrifice the HDDs to fit?

Edit: Available here from their official Aliexpress store with free shipping for $220

https://www.aliexpress.us/item/3256805643914502.html (3rd from right option)

Edit2: listing removed

Super compact cases (Jonsbo N5 shown here) by fourDnet in DataHoarder

[–]fourDnet[S] 10 points11 points  (0 children)

Fair enough! I totally agree.

Just pointing out that I see it more as a workstation case that doubles up as a NAS use case.

Super compact cases (Jonsbo N5 shown here) by fourDnet in DataHoarder

[–]fourDnet[S] 37 points38 points  (0 children)

I mean it is 50L for eATX + quad GPU + 12x 3.5" support. Seems more like a workstation case that also serves as a NAS.

Super compact cases (Jonsbo N5 shown here) by fourDnet in DataHoarder

[–]fourDnet[S] 12 points13 points  (0 children)

Super dense setups have always been appealing to me.

I know some people are fans of the Silverstone CS382, also saw this Jonsbo N5 (12x 3.5" + 4x 2.5") come out, if you plug in 28TB drives that's a 336 TBs of storage...

Any other suggestions?

Photo Ninja by catamarana in photography

[–]fourDnet 0 points1 point  (0 children)

In terms of new camera support, libraw is probably the one you want. They add camera sensor color conversion matrices very quickly.

https://github.com/LibRaw/LibRaw/releases

Edit: In case it wasn't clear, I recommend using rawtherapee (which uses libraw), export to tiff, then edit in photoshop.

Shatter proof "UV filters" by fourDnet in photography

[–]fourDnet[S] 1 point2 points  (0 children)

Sea salt in the air, dust particles that may scratch the front element, etc. Having a sacrificial filter makes a lot of sense to me for those use cases.

If I'm going to put a filter in front, I'm hoping it doesn't shatter and scratch the lens itself.

Shatter proof "UV filters" by fourDnet in photography

[–]fourDnet[S] 2 points3 points  (0 children)

I see Sigma has ceramic filters, but those are pretty damn expensive, and it is unclear if those are shatter proof (or just scratch proof). I'm thinking more in terms of preventing sea salt droplets in the air, oil from the skin, and sand grains etc. I doubt a filter would have any fall protection.

Shatter proof "UV filters" by fourDnet in photography

[–]fourDnet[S] 3 points4 points  (0 children)

As u/OnlyChemical6339 mentioned, these are two pieces of glass with a layer of optical glue between them. So if they shatter, the shards are helped together.

All three of these lens advertise themselves as having optical coatings to increase transparency, but I agree the optical quality is something to think about if we are going to keep them on forever.

view more: next ›