"How did you get hired?"
"I was top at Fortnite. You?"
"Combat flight sims."

"Damn, two Boeings just crashed over an oil refinery on city limits and took out half the city."
"Did you remember to save your position beforehand?"
"Yeah."
"The reload and continue from there. No-one will notice."

I love computer games. I have XPlane 12 and many scenery packs. I rank well on Elite:Dangerous. From the sounds of it, the FAA would see me as over-qualified. In reality? There's no way in hell I'd be taking those kinds of risks with real lives. There's a huge difference between having good reflexes and a good eye, versus having the complex 4D spacetime relationship mental models needed for robust air traffic control.

Freenet is also based on middle-out compression.

No. Trying to push 30TB into Freenet will be interpreted as an attack on the network by other peers and they'll disconnect from your peer.

Freenet is better viewed as a communication medium than a storage medium. Data is prioritized according to demand, somewhat like an LRU cache. You can't upload 30TB and expect the network to preserve it for you.

On liability, running a Freenet peer is more like running network infrastructure that automatically routes and caches other people's traffic than intentionally publishing it. US law explicitly recognizes this distinction in the DMCA's provisions for transitory network communications and system caching. We discuss this in more detail in the FAQ.

You obviously haven't tried Freenet. You can join our room on River and you'll see no spam, no fraud, no hate speech, none of that.

Decentralized doesn't mean unmoderated. Systems built on Freenet can be moderated however their creators and communities want. River is moderated.

The difference is that nobody running one service gets to decide what everyone else is allowed to run.

1) 30B, dense. Optimized to fit Q4 quantized in a 24GB card with a speculative decoding model as well.

2) Because reporters don't know what weights are and assume you don't know either.

3) Way better than Gemma 4 on text tasks, slightly better on multimodal. Numbers below are all: Benchmark: Muse Glimmer score Gemma 4 31B score difference

Artificial Analysis Intelligence Index: Muse Glimmer: 35 30 +5
MCP Atlas (Public): 75.5 54.2 +21.3
DeepSearch QA: 74.6 61.7 +12.9
SWE-Bench Pro: 51.2 36.9 +14.3
SWE-Bench Verified: 76.0 66.6 +9.4
OSWorld-Verified: 65.9 58.5 +7.4
GAIA2: 43.3 36.4 +6.9
WildClawBench: 47.6 37.6 +10.0
TerminalBench 2.1: 51.7 43.4 +8.3
Tau3-Banking: 23.5 15.1 +8.4
MMMU Pro: 74.0 73.0 +1.0
Charxiv Reasoning: 78.8 77.7 +1.1
OmniDocBench v1.5: 75.8 72.5 +3.3
ScreenSpot Pro: 75.4 75.9 -0.4

4) 128k tokens

5) No, sadly.

What you want is a DGX Spark.

It costs $4000.

And yes, there are fundamental advantages to cloud services, such as large-scale batching, little idle downtime, high speed, and hardware optimized to the specific models / serving needs. That said, one can weigh that off against sovereign control over your server...

Define "tolerable".

The model in question - Muse Glimmer - with DFlash/speculative decoding - will probably get you ~60 to 124 tok/s on a 3090 (a quite dated GPU). For a 5090, it's said to clock in at 233,4 tok/s. On CPU you're looking at maybe 3-5 tok/s.

If you call that "tolerable", I guess you're more patient than me? And as mentioned, you're not just wasting time, but also wasting a lot of power too - CPU is a very power-inefficient way to run ML models.

If you insist on CPU, this isn't the right kind of model anyway. You want to take advantage of the fact that you probably have lots of (comparably cheap) RAM, and compensate for the fact that you have (comparably) terrible memory bandwidth, and for that, you want a MoE with a high total parameters but a low active parameters. Not a dense model like this.

Then I was basically aiming at Mac Minis

That's very much a special case which you didn't mention in your post that I responded to, but still the answer is "meh". You couldn't run it at all on a 16GB Mac Mini, and I think you'd struggle to run it at all on a 24GB (because you have to share the ram with the OS, the inference server, etc). For the base Mini you might get 10-12 tok/s, and for the M2 Pro / M4 Pro, maybe 25-35 tok/s. Still pretty far from a GPU, though.

This model is designed for >= 24GB GPUs.

MoEs don't save RAM (for a given quality), they increase it. You have to store all of the parameters in memory, not just the active params. But inference only uses a subset of the total params for each token, so it reduces the memory bandwidth requirements and improves token generation rate. But this comes at the cost of a higher total param count for a given quality.

Meta has always been releasing open models (Llama was famously the first powerful open model). The change has actually been in the opposite direction, with Muse Spark *not* being open.

Zuck descrbed his motivation way back when, about how they got burned with Facebook on app stores, in that Apple and Google could basically bully them however they wanted, on whatever extractive terms they wanted, and there was nothing Meta could do about it. He's now paranoid about "others controlling the platform", and wanted to make sure that doesn't happen with AI, that they have their own AI base to work with.

I mean, if you want your model to be terribly slow and terribly power inefficient, sure, go ahead and run it on CPU.

This really isn't a good model for CPU. For CPU, you want a MoE with a large number of total params but a tiny number of active params. Something like DeepSeek V4 Flash 0731 if you have at least 128GB of RAM - you might get 2-3 tok/s or so on that. The goal is to minimize the memory bandwidth requirements per token, at the cost of a greater total RAM footprint.

For GPU, you're highly VRAM limited but not bandwidth limited, so your best option is generally a dense model (non-MoE) with speculative decoding to make up for the performance limitations.

You can use it fine on 24GB with a good size context with KV cache compression. They've also designed it to use speculative decoding to boost performance instead of a MoE, so that they can pack as much capability as possible into a dense model. Seems like a winner.

You see, Linus has embraced transhumanism and is testing out the new kernel as a supplementary brain function, using MOSIX to offload all of the irritable comment generation at yet more nonsense on the mailing list to an Elizabot that he has written specifically to do this. This saves his actual brain for real work.

AI is not currently capable of performing any meaningful conceptual abstraction, it is only capable of very basic mechanical abstraction. Abstraction is itself multi-dimensional. Nor is there any indication that AI could ever perform multi-dimensional abstraction or multi-dimensional decomposition. Precious few humans are capable of it either, but some can.

No, AI would need humans because the best system is not a pure system but a hybrid system, and that means transhumanism with AI operating as an additional brain function rather than as a replacement for a brain.