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Groups > comp.lang.prolog > #14923
| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Newsgroups | comp.lang.prolog |
| Subject | Give Julio Di Egidio the bloody money [3 RMB¥ MiniMind] (Re: Eat Tteokbokki before SkyNet kills you [$100 ChatGPT]) |
| Date | 2025-10-18 16:19 +0200 |
| Message-ID | <10d07m6$17pvl$2@solani.org> (permalink) |
| References | (1 earlier) <10cmqea$s93$1@solani.org> <10cn157$10qq$1@solani.org> <10co9kp$12u6u$1@solani.org> <10coa13$12ucf$1@solani.org> <10d06ch$17p4u$1@solani.org> |
Hi, Give Julio Di Egidio the bloody money. He is craving for 300 USD so that he can buy the ISO Prolog core standard. Just imagine he would want to build a MiniMind. Just lets put some more prespective on the current costs: This open-source project aims to train a super-small language model MiniMind with only 3 RMB cost and 2 hours, starting completely from scratch. The MiniMind series is extremely lightweight, with the smallest version being 1/7000 the size of GPT-3, making it possible to train quickly on even the most ordinary personal GPUs. https://github.com/jingyaogong/minimind/blob/master/README_en.md ChatGPT tells me that most of the numbers are correct when you rent a GPU by the hour. But what about a 100% ownership of a GPU for a year. I find this might cost 12'000 USD. One has to separate platforms for execution from those platforms for training: GEX44: for AI inference Nvidia RTX™ 4000, 184 EUR / month GEX130: for AI training NVIDIA RTX™ 6000, 813 EUR / month https://www.hetzner.com/dedicated-rootserver/matrix-gpu/ Bye Mild Shock schrieb: > Hi, > > Thinks are definitively accelerating. I really would > like to use an AI that knows about all the News of today. > This bloody cut date is so annoying. > > Further indicative that AI is accelerating: > > In August 2025, Sam Altman dropped a bombshell: > > *months, not years: Rushing GPT-6* > In August 2025, Sam Altman dropped a bombshell: > GPT-6 is already in development and coming sooner > than you think. Not in two years, but > potentially in months. > https://www.youtube.com/watch?v=44mJb5sKji0 > > Karpathy, coined vibe coding, released in October 2025: > > *nanochat: The best ChatGPT that $100 can buy* > This repo is a full-stack implementation of an > LLM like ChatGPT in a single, clean, minimal, > hackable, dependency-lite codebase. nanochat is > designed to run on a single 8XH100 node via > scripts like speedrun.sh, that run the > entire pipeline start to end. > https://github.com/karpathy/nanochat > > Bye > > Mild Shock schrieb: >> Hi, >> >> But not only Mobie AI and Desktop AI are making >> a broader imprint now. We might also experience >> Workstation AI, with a 3'000.- USD price tag: >> >> > You Can't Buy This... Yet! The NVIDIA GB10 from Dell >> > The New Superchip that Terrifies the Cloud! >> > https://www.youtube.com/watch?v=x1qViw4xyVo >> >> So whats going on? I was asking Phind, which is >> driven by a 70B model tailored towards developers: >> >> Q: Is there an AI inflection point right now , >> with NPUs in mobile, desktop and workstation >> >> A: Evidence of the Inflection Point >> >> - Mobile Leadership >> NPUs originated in smartphones >> Now becoming ubiquitous across all device types >> Enabling sophisticated AI features at consumer price points >> >> - Desktop Revolution >> Major manufacturers implementing NPUs across product lines >> Apple's Neural Engine integrated into M-series chips >> Qualcomm, Intel, and AMD incorporating AI accelerators >> >> - Workstation Transformation >> Professional-grade NPUs in mobile workstations >> Demonstrated superior performance for AI-specific tasks >> Enabling local processing of previously cloud-dependent workloads >> >> https://www.phind.com/search/cmgs1s6jv00023h67g5z2aaa0 >> >> Bye >> >> Mild Shock schrieb: >>> Hi, >>> >>> It seems I am having problems pacing with >>> all the new fancy toys. Wasn't able to really >>> benchmark my NPU from a Desktop AI machine, >>> >>> picked the wrong driver. Need to try again. >>> What worked was benchmarking Mobile AI machines. >>> I just grabbed Geekbench AI and some devices: >>> >>> USA Fab, M4: >>> >>> sANN hANN qANN >>> iPad CPU 4848 7947 6353 >>> iPad GPU 9752 11383 10051 >>> iPad NPU 4873 36544 *51634* >>> >>> China Fab, Snapdragon: >>> >>> sANN hANN qANN >>> Redmi CPU 1044 950 1723 >>> Redmi GPU 480 905 737 >>> Redmi NNAPI 205 205 469 >>> Redmi QNN 226 226 *10221* >>> >>> Speed-Up via NPU is factor 10x. See the column >>> qANN which means quantizised artificial neural >>> networks, when NPU or QNN is picked. >>> >>> The mobile AI NPUs are optimized using >>> mimimal amounts of energy, and minimal amounts >>> of space squeezing (distilling) everything >>> >>> into INT8 and INT4. >>> >>> Bye >>> >>> Mild Shock schrieb: >>>> Hi, >>>> >>>> The change from 378 ms to 286 ms is around 25-30% >>>> is insane. But I did both tests on a novel AI CPU. >>>> To be precise on a AMD Ryzen AI 7 350. >>>> >>>> But somehow I picked up rumors that AI CPUs now >>>> might do Neural Network Branch Prediction. The >>>> idea seems to exist in hardware at least since (2012): >>>> >>>> Machine learning and artificial intelligence are >>>> the current hype (again). In their new Ryzen >>>> processors, AMD advertises the Neural Net >>>> Prediction. It turns out this is was already >>>> used in their older (2012) Piledriver architecture >>>> used for example in the AMD A10-4600M. It is also >>>> present in recent Samsung processors such as the >>>> one powering the Galaxy S7. What is it really? >>>> https://chasethedevil.github.io/post/the_neural_network_in_your_cpu/ >>>> >>>> It can be done with Convoluted Neural Networks (CNN): >>>> >>>> BranchNet: A Convolutional Neural Network to >>>> Predict Hard-To-Predict Branches >>>> To this end, Tarsa et al. proposed using convolutional >>>> neural networks (CNNs) that are trained at >>>> compiletime to accurately predict branches that >>>> TAGE cannot. Given enough profiling coverage, CNNs >>>> learn input-independent branch correlations. >>>> https://microarch.org/micro53/papers/738300a118.pdf >>>> >>>> Interstingly the above shows cases a PGO based >>>> Machine Learning for Branch Predictors. No clue >>>> how they construct the CPU, that they can feed >>>> >>>> it with offline constructed neural neutworks for >>>> their own execution. Maybe an optimizer uses it? >>>> But I guess a more modern solutions would not only >>>> >>>> use CNN, but also an Attention Mechanism. >>>> >>>> Bye >>>> >>>> Mild Shock schrieb: >>>>> Hi, >>>>> >>>>> I spent some time thinking about my primes.pl >>>>> test. And came to the conclusion that it >>>>> mainly tests the Prolog ALU. Things like >>>>> >>>>> integer successor or integer modulo. Then >>>>> I found that Java has Math.floorMod() which >>>>> I wasn't using yet. And peng results are better: >>>>> >>>>> /* Dogelog Player 2.1.2 for Java, today */ >>>>> ?- time(test). >>>>> % Zeit 286 ms, GC 1 ms, Lips 26302430, Uhr 15.10.2025 02:31 >>>>> true. >>>>> >>>>> Maybe the Java backend picks a CPU instruction >>>>> for Math.floorMod() instead of executing the >>>>> longer code sequence that is needed to correct >>>>> >>>>> rem/2 into mod/2. Who knows. I also reorganized >>>>> the code a little bit, and eliminated an extra >>>>> method call in all arithmetic functions, by >>>>> >>>>> inlining the arithmetic function body in the >>>>> evaluable predicate definition code. Comparison >>>>> to old measurements and some measurements of >>>>> >>>>> other Prolog systems: >>>>> >>>>> /* Dogelog Player 2.1.2 for Java, weeks ago */ >>>>> ?- time(test). >>>>> % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44 >>>>> true. >>>>> >>>>> /* SWI-Prolog 9.0.4 */ >>>>> ?- time(test). >>>>> % 7,506,639 inferences, 0.363 CPU in 0.362 seconds >>>>> (100% CPU, 20693560 Lips) >>>>> true. >>>>> >>>>> /* Scryer Prolog 0.9.4-639 */ >>>>> ?- time(test). >>>>> % CPU time: 0.365s, 7_517_613 inferences >>>>> true. >>>>> >>>>> /* Trealla Prolog 2.82.23-3 */ >>>>> ?- time(test). >>>>> % Time elapsed 0.868s, 11263917 Inferences, 12.983 MLips >>>>> true. >>>>> >>>>> Bye >>>>> >>>>> P.S.: The code uses the hated mathematical mod/2, >>>>> and not the cheaper rem/2 that CPUs usually have: >>>>> >>>>> test :- >>>>> len(L, 1000), >>>>> primes(L, _). >>>>> >>>>> primes([], 1). >>>>> primes([J|L], J) :- >>>>> primes(L, I), >>>>> K is I+1, >>>>> search(L, K, J). >>>>> >>>>> search(L, I, J) :- >>>>> mem(X, L), >>>>> I mod X =:= 0, !, >>>>> K is I+1, >>>>> search(L, K, J). >>>>> search(_, I, I). >>>>> >>>>> mem(X, [X|_]). >>>>> mem(X, [_|Y]) :- >>>>> mem(X, Y). >>>>> >>>>> len([], 0) :- !. >>>>> len([_|L], N) :- >>>>> N > 0, >>>>> M is N-1, >>>>> len(L, M). >>>>> >>>>> Mild Shock schrieb: >>>>>> Hi, >>>>>> >>>>>> WebPL is already outdated I guess. It doesn't >>>>>> show the versions of the other Prolog systems >>>>>> it is using. While I had these results for >>>>>> >>>>>> the primes example in the WebPL playground: >>>>>> >>>>>> /* Trealla Prolog WASM */ >>>>>> (23568.9ms) >>>>>> >>>>>> When I run the example here: >>>>>> >>>>>> https://php.energy/trealla.html >>>>>> >>>>>> I get better results: >>>>>> >>>>>> /* trealla-js 0.27.1 */ >>>>>> >>>>>> ?- time(test). >>>>>> % Time elapsed 9.907s, 11263917 Inferences, 1.137 MLips >>>>>> >>>>>> Bye >>>>> >>>> >>> >> >
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WebPL is already outdated Mild Shock <janburse@fastmail.fm> - 2025-08-17 18:37 +0200
Heap/Stack versus WAM resp. ZIP (Was: WebPL is already outdated) Mild Shock <janburse@fastmail.fm> - 2025-08-18 14:52 +0200
Trealla knows Program Sharing (PS) Tricks ? (Was: Heap/Stack versus WAM resp. ZIP) Mild Shock <janburse@fastmail.fm> - 2025-08-18 15:06 +0200
Smarter Partial Strings would use Program Sharing ['$append'/3] (Was: Trealla knows Program Sharing (PS) Tricks ?) Mild Shock <janburse@fastmail.fm> - 2025-08-18 15:42 +0200
Static Shunting is even better [Dogelog Player] (Was: Smarter Partial Strings would use Program Sharing ['$append'/3]) Mild Shock <janburse@fastmail.fm> - 2025-08-18 15:49 +0200
The Artificial Intelligence Flip: Acer Swift Go! (Was: WebPL is already outdated) Mild Shock <janburse@fastmail.fm> - 2025-08-31 23:56 +0200
2025 will be last year we hear of Python (Re: The Artificial Intelligence Flip: Acer Swift Go!) Mild Shock <janburse@fastmail.fm> - 2025-09-01 00:45 +0200
Apertus: With love, from Switzerland [02 Sept 2025] (Re: 2025 will be last year we hear of Python) Mild Shock <janburse@fastmail.fm> - 2025-09-05 00:36 +0100
Don't try this (Was: Apertus: With love, from Switzerland [02 Sept 2025] ) Mild Shock <janburse@fastmail.fm> - 2025-09-05 01:03 +0100
AI means ambracing Non-Linearity (Was: 2025 will be last year we hear of Python) Mild Shock <janburse@fastmail.fm> - 2025-09-19 10:01 +0200
AI soaked PCs: Is there a Copilot+ Prolog? (Was: AI means ambracing Non-Linearity) Mild Shock <janburse@fastmail.fm> - 2025-09-19 10:10 +0200
The morning coffee incident [Prolog Community] (Was: AI soaked PCs: Is there a Copilot+ Prolog?) Mild Shock <janburse@fastmail.fm> - 2025-09-19 14:38 +0200
Root Cause Prediction for Your Brain (Was: The morning coffee incident [Prolog Community]) Mild Shock <janburse@fastmail.fm> - 2025-09-19 18:22 +0200
Please delete my account and all my posts on SWI-Prolog discourse (Re: Root Cause Prediction for Your Brain) Mild Shock <janburse@fastmail.fm> - 2025-09-19 18:38 +0200
I will consult a Lawyer of mine (Was: Please delete my account and all my posts on SWI-Prolog discourse) Mild Shock <janburse@fastmail.fm> - 2025-09-19 18:42 +0200
Scryer Prolog unify_with_occurs_check/2 doesn't scale (Was: WebPL is already outdated) Mild Shock <janburse@fastmail.fm> - 2025-09-19 16:08 +0200
How bad is Rust, can JavaScript beat it? (Was: Scryer Prolog unify_with_occurs_check/2 doesn't scale) Mild Shock <janburse@fastmail.fm> - 2025-09-19 16:18 +0200
unify_with_occurs_check/2 might have been fixed (Was: Scryer Prolog unify_with_occurs_check/2 doesn't scale) Mild Shock <janburse@fastmail.fm> - 2025-09-25 01:50 +0200
Had to Rollback my Jaffar Unification (Was: unify_with_occurs_check/2 might have been fixed) Mild Shock <janburse@fastmail.fm> - 2025-09-25 01:59 +0200
Trealla Prolog might apply "frozeness" to cyclic terms (Was: Had to Rollback my Jaffar Unification) Mild Shock <janburse@fastmail.fm> - 2025-09-25 02:06 +0200
Non-intrusive through "frozen" subcategories (Was: Trealla Prolog might apply "frozeness" to cyclic terms) Mild Shock <janburse@fastmail.fm> - 2025-09-25 02:21 +0200
Scryer Prolog occurs check cannot do hydra (Was: Scryer Prolog unify_with_occurs_check/2 doesn't scale) Mild Shock <janburse@fastmail.fm> - 2025-09-26 12:19 +0200
WebPL and Scryer Prolog are bad examples (Was: WebPL is already outdated) Mild Shock <janburse@fastmail.fm> - 2025-10-13 09:49 +0200
Who will win Shift-Reduce or Tabled DCG? [AI Boom] (Was: WebPL and Scryer Prolog are bad examples) Mild Shock <janburse@fastmail.fm> - 2025-10-13 15:09 +0200
primes.pl mainly tests the Prolog ALU [mod/2 vs rem/2] (Was: WebPL is already outdated) Mild Shock <janburse@fastmail.fm> - 2025-10-15 02:38 +0200
25-30% is insane, Neural Network Branch Prediction? (Was: primes.pl mainly tests the Prolog ALU) Mild Shock <janburse@fastmail.fm> - 2025-10-15 04:33 +0200
NPUs (Neural Processing Units) are the new normal (Was: 25-30% is insane, Neural Network Branch Prediction?) Mild Shock <janburse@fastmail.fm> - 2025-10-15 16:04 +0200
Ask Phind: AI inflection point right now [End 2025] (Was: NPUs (Neural Processing Units) are the new normal) Mild Shock <janburse@fastmail.fm> - 2025-10-15 16:10 +0200
Eat Tteokbokki before SkyNet kills you [$100 ChatGPT] (Was: Ask Phind: AI inflection point right now [End 2025]) Mild Shock <janburse@fastmail.fm> - 2025-10-18 15:57 +0200
Give Julio Di Egidio the bloody money [3 RMB¥ MiniMind] (Re: Eat Tteokbokki before SkyNet kills you [$100 ChatGPT]) Mild Shock <janburse@fastmail.fm> - 2025-10-18 16:19 +0200
Vertex AI Training is more expensive? (Was: Give Julio Di Egidio the bloody money [3 RMB¥ MiniMind]) Mild Shock <janburse@fastmail.fm> - 2025-10-21 00:32 +0200
The Love Affair: OpenAI and AMD (Was: NPUs (Neural Processing Units) are the new normal) Mild Shock <janburse@fastmail.fm> - 2025-10-18 18:59 +0200
The NPU in your Browser [WebNN by W3C] (Was: NPUs (Neural Processing Units) are the new normal) Mild Shock <janburse@fastmail.fm> - 2025-10-26 08:39 +0100
Fuzzy Alert: Boris the Loris on the Dancefloor (Was: The NPU in your Browser [WebNN by W3C]) Mild Shock <janburse@fastmail.fm> - 2025-10-26 11:33 +0100
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