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#gpu

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@maikel basically, it boils down to the few key features of #Monero:

1. #Anonymity & #Privacy: Unlike with any other #cryptocurrency (aka. #Shitcoins) it's not just pseudonymous in that there is no mandatory linkage between individuals & their wallets, but the entire transaction history and balance is hidden. Unlike say #Bitcoin or #Ethereum one cannot track the coins from the moment of mining to their destination.

2. Speed: Monero's network does mine one block every 2 minutes. After 10 blocks any transfered balance gets unlocked for spending. That means that a transfer is completed at worst within 6 minutes and the balance is being unlocked at worst after 24 minutes. This makes it faster than Instant-#SEPA which only has a 1 hour SLA.

3. #Fungibility: Like #cash all it's coins are equal, since they cannot be tracked. This makes Monero the digital equivalent of cash.

4. #Scalability & #Stability: Monero adaptively self-adjusts block sizes and mining difficulty based upon demand (transactions in it's mempool aka. requested transactions that have to be added to the blockchain) and supply (total blockchain hashrate). Unlike Bitcoin and Ethereum it has a fixed Tail Emission Rate of at least 0,6 #XMR (Monero) per block, so the miners solving it get at least 0,6 XMR (+ transaction fees), which is a longterm stable rate. Bitcoin and Ethereum will necessitate huge transfer fees once their last coins are mined to make sense, which will result in the crash of said cryptocurrencies as they'll be too expensive to trade!

5. Anti-#ASIC and focussed on #CPU|s of general-purpose machines: Whilst it does run on #ProofOfWork, it's specifically designed to run poorly on #GPU|s and not on #ASICs as the latter one are not just manufactured #eWaste but also inherently increase the centralization (with less than a dozen big miners controlling >50% of Bitcoin and Ethereum's hashrate respectably). Thus it's the "least worst" in that regard. #ProofOfStake is not possible due to it's privacy-based setup (#Staking necessitates a public balance) and unlike a #Shitcoin like #FileCoin it doesn't incentivize #hoarding components. (in this case: #HDD|s)

6. Accepted & Convertable: Whilst there is a concerted effort to ban Monero, there are payment processors like #NowPayments that accept Monero. It's low transaction fees and good speed make it useable in settings like Restaurants and Online Stores (sadly not retail, because it would need to be like 60x faster)... And even then it's easy to convert to/from Shitcoins.

That's the #TLDW of Whiteboard Crypto, Mental Outlaw and The Hated One

And finally:

7. Monero gets continously developed and enhanced, whereas Bitcoin, #Litecoin and Ethereum don't even do proper #upgrades via #HardForks (see #EthereumClassic)...

It's convenient that I can use #LLMs to help me learn how to use LLMs because I'm pretty sure I wouldn't be able to figure it out any other way.

I want to use my local #Ollama models with #Copilot in #VSCode, but I have an #AMD #GPU so apparently I need to install something called the #ROCm (Radeon Open Compute Platform) via the Windows 11 HIP SDK?

And maybe all this doesn't work in #WSL, so I'll have to reinstall it in #Ubuntu there if I want to use it in one of those workspaces?

Can you program GPUs and do you want to become a HERO? #linuxphone
community needs your help.

We are trying record video, and have most pieces working, but one is
missing: fast enough debayering. That means about 23MB/sec on #librem5.

Debayering is not hard; camera images have subpixels split on two
lines, which need to be corrected. They also use different color
representation, but that's fixable by some table lookup and two matrix
multiplies.

Librem 5 has Vivante GPU, 4 in-order CPU cores and 3GB RAM. My feeling
is that it should be fast enough for that. If task is for some reason
impossible, that would be good to know, too.

Image data looks like this

RGRGRG...
xBxBxB...
.........
.........

Task is to turn that into usual rgbrgb.... format. rgb = RGB * color
matrix, with table lookups for better quality. I can fix that once I
get an example.

I'm looking for example code (#pinephone would work, too), reasons it
can not be done... and boosts if you have friends that can program
GPUs. #gpu #opensource

People continue to think about #AI in terms of #2010s computing, which is part of the reason everyone gets it wrong whether they're #antiAI or #tech bros.

Look, we had 8GB of #ram as the standard for a decade. The standard was set in 2014, and in 2015 #AlphaGo beat a human at #Go.

Why? Because, #hardware lags #software - in #economic terms: supply follows demand, but demand can not create its own supply.

It takes 3 years for a new chip to go through the #technological readiness levels and be released.

It takes 5 years for a new #chip architecture. E.g. the #Zen architecture was conceived in 2012, and released in 2017.

It takes 10 years for a new type of technology, like a #GPU.

Now, AlphaGo needed a lot of RAM, so how did it stagnate for a decade after doubling every two years before that?

In 2007 the #Iphone was released. #Computers were all becoming smaller, #energy #efficiency was becoming paramount, and everything was moving to the #cloud.

In 2017, most people used their computer for a few applications and a web browser. But also in 2017, companies were starting to build #technology for AI, as it was becoming increasingly important.

Five years after that, we're in the #pandemic lockdowns, and people are buying more powerful computers, we have #LLM, and companies are beginning to jack up the const of cloud services.

#Apple releases chips with large amounts of unified #memory, #ChatGPT starts to break the internet, and in 2025, GPU growth continues to outpace CPU growth, and in 2025 you have a competitor to Apple's unified memory.

The era of cloud computing and surfing the #web is dead.

The hype of multi-trillion parameter #LLMs making #AGI is a fantasy. There isn't enough power to do that, there aren't enough chips, it's already too expensive.

What _is_ coming is AI tech performing well and running locally without the cloud. AI Tech is _not_ just chatbots and #aiart. It's going to change what you can do with your #computer.

#NVIDIA #TensorCore Evolution: From Volta To Blackwell Amdahls Law, Strong Scaling, Asynchronous Execution, Blackwell, Hopper, Ampere, Turing, Volta, TMA
They introduce core features of major #datacenter #GPU, first explaining important first principles of performance engineering. Then trace evolution of Nvidia’s Tensor Core architectures and programming model, highlighting motivations behind evolution. End goal is to provide a resource for understanding Nvidia’s GPU arch
semianalysis.com/2025/06/23/nv

SemiAnalysis · NVIDIA Tensor Core Evolution: From Volta To BlackwellIn our AI Scaling Laws article from late last year, we discussed how multiple stacks of AI scaling laws have continued to drive the AI industry forward, enabling greater than Moore’s Law grow…