#Mixture-of-Experts (MoE)

[revista-amazonia]

Meta Lança o Multimodal Llama 4

A Meta anunciou oficialmente a revolucionária série Llama 4, trazendo ao mercado modelos de IA nativamente multimodais, com capacidades avançadas para processar texto, imagens e vídeos. Segundo o Tom’s Guide, esta nova geração de modelos promete um salto significativo na tecnologia de inteligência artificial, com capacidades de raciocínio aprimoradas e a habilidade de agentes de IA utilizarem…

[kazifatagar]

AI Revolution from China: DeepSeek Unveils World's Most Efficient AI Model, Shaking Tech Giants

DeepSeek, a Chinese AI startup, has emerged as a significant player in the AI landscape, particularly with its latest models like DeepSeek-V3 and DeepSeek-R1. Here’s an analysis of what’s behind DeepSeek’s secret, why it caused such a shock, and what makes it powerful: Innovative Model Architecture and Efficiency: Mixture of Experts (MoE): DeepSeek-V3 uses a 671 billion parameter model with a…

[rosehearrt]

for the character meme, the two green-heads: trey and / or sebek!

ACCEPTING ━ Give Me a Character;

Sebek Zigvolt.

How I feel about this character

A lot of people don't know this about me, but I actually really like Sebek! At first I was a little bit uninterested, but the more I got to know him and his character, the more endearing and adorable he became to me. Riddle's fondness for him rubbed off on me! Haha just kidding, but really, watching him struggle to grow and adapt to life outside of the Valley has been like watching a train wreck in slow motion in the best of ways. He's so loud and ridiculous but as the story's progressed, I think he's calmed down a considerable amount. Watching him bond so much with humans who he previously thought of as so inferior to himself has also been a huge highlight to me, and it warms my heart to see his perspective changing. I also love me some good gap-moe, so I adore the juxtaposition between his big buff appearance and some of his more innocent character traits, like the way he gets overexcited or overly serious about things, or the hero worship he has for Malleus. He's my favorite himbo and I just want to kiss his little face!

All the people I ship romantically with this character

sebeshiru / sebesil: I have to be honest, it was fan art that got me into this. But now that I am into it, there's no going back. I absolutely adore the idea of Sebek continuing to borderline bully Silver over his status as a human, and just be a jerk in general until one day he realizes ' wow, his eyelashes are really long... ' and then ' his hair is so soft and shiny ' and before he even realizes it, he's completely fallen for Disney princess Silver. I love the idea of him taking forever to come to terms with that fact, but once he does, steeling himself and very seriously declaring his intentions regarding courting to Silver. I love the idea of them having a romantic moment on one of the Diasomnia balconies where Sebek decides to take a knee and tell him that while he he will primarily be Malleus's knight, he wants to pledge on his sword to always protect Silver as well. I love him going to Lilia and getting his blessing. Another thing I love is the idea of him and Silver being very culturally traditional. Sebek wore a hakama/montsuki hakama for New Year's and both he and Silver wore a qingdai guanmao with a mamianqun ( which was traditionally worn by women lol ), and what appears to be a yunjian ( don't quote me on this because I'm not an expert on chinese history but it is an interest ) and basically just a bunch of different fashion influences of the Qing Dynasty thrown together for halloween, so I definitely feel like the Diasomnia characters are presented with hints of an east asian heritage, maybe a mixture of Japanese and Chinese culture to create something TWST equivalent of the two cultures like Yana seems to have done for places such as the Middle East and Africa. But anyway. I'm getting sidetracked here. My point is I love the idea of the two of them involving traditional East Asian courtship methods in their relationship. Like Chinese courtship, for example, where they would be receiving the guidance of an astrologer, exchanging tea presents, etc. I'd love to see a deeper dive into the cultures Diasomnia students hail from! And for some reason I can really see Silver and Sebek as a couple feeling passionate about honoring those cultures. 10/10 also love picturing the two of them waking up in the same bed in the future and going about their morning routines together, trading soft conversation and kisses as they don their armor and prepare for a day of serving their prince as his two most talented knights.

maresebe / mallesebe: I have nothing to say for myself beyond that I love the idea of hero-worshipper getting to date his hero and constantly pinching himself over it and melting into a bumbling unintelligible mess. And then, later in life, the prince being married to his knight. Sebek already adores Malleus so much, being with him would be a dream come true. I think he'd try not to allow himself to become spoiled, but Malleus would encourage it because he thinks Sebek is cute when he gets clingy. As for Malleus, he'll have a lover hopelessly devoted to him and who adores him beyond belief, so that's a pretty nice perk. It's also super funny because both of them are incredibly naive in certain ways ( or maybe it should be phrased as being behind in learning for Malleus, since I don't know if I would use naive to describe him ), so I think they'd be the couple doing odd things like wearing the wrong outfits to certain places or trying to buy the other a painting at a museum because the other likes it. Both just not understanding how the modern world works at all and standing out completely no matter where they are. I love that for them.

My non-romantic OTP for this character

lilia: Oh Lilia, the unofficial second parent for everyone in Diasomnia ( aside from his own son oc, he's the number one parent for him ). I love the way Sebek is so sincere in his admiration of Lilia, and Lilia's response is to tease him and play pranks. I fully believe that's been going on since Sebek was a child, and I also fully believe he went through a phase where he thought Lilia was a vampire and would cry when he saw him. But despite all the teasing, Lilia seems to really care about his boys, and the fact that he is playful with Sebek reflects ( imo ) a type of patience for some of his more childish ways, such as insulting Silver over his status as a human or getting too much in Malleus's face with his idolization. I definitely don't think he'd allow anyone other than Sebek to act badly towards Silver dhsgjryfsu and he's more than capable of providing swift punishment, but he clearly considers Sebek close enough to not do that. I think Lilia has lived a long and lonely life, and while Silver is the main love of his life ( being his child who he canonically talks about in a very doting manner ), clearly Malleus and Sebek are important to him as well. I think creating this pseudo family through the three kids he somehow gathered as his own has probably provided him with a lot of purpose and joy that he might not have had in the same way before. I don't have too much to say beyond that since Lilia is one of those character who - while I enjoy him - I don't think about all that much in terms of HCs since he isn't really one of the characters that I deeply connect with. But I do love the Diasomnia family dynamic, and I think it's really wonderful that Sebek has people watching over him who know him as an individual, especially a parental figure like Lilia.

riddle: I'm putting Riddle for so many of these omg djhgesgdryfjs I PROMISE IT'S NOT JUST BC HE'S MY MUSE HAHAHA, I love him and Sebek though. Their relationship is just beautiful to me, because it provides exceptions to both of their extremely rigid worldviews. The game makes it a point to talk about Sebek's thoughts of the inferiority of humankind over and over, and yet he changed his tune very quickly upon meeting Riddle, and now deeply respects him and seems wholly comfortable around him. As for Riddle, he generally dislikes loud and hyper people that he has difficulty controlling, but Sebek's willingness to listen and learn from him, and the respect he shows him has endeared Riddle to him greatly. I think that for someone like Riddle who has such difficulty sorting through his relationships, how to label them, their true nature, etc, Sebek and Silver have both become important individuals to him, but his relationship with Sebek is unique. Riddle struggles often to get on the same wavelength as his own first year students, and at this point in time ( post-overblot ), the majority of his dorm still dislikes him. But Sebek turns his admiration-filled eyes on Riddle, and Riddle likely gets blown over by them every time. This isn't to say he isn't strict - his regimen for club is probably just as militant as the way he upholds tradition in his dorm. But I think he has a huge soft spot for Sebek nonetheless. I also think, that in a way, looking at Sebek is a little bit like looking at a younger version of himself. Stubborn and stuck in his ways, obsessed with tradition and having things be done only the way he believes they should ( Riddle is definitely still like this to a degree, but I think he's calmed down a bit whereas Sebek is still in the yelling and rioting stage ), and prone to outbursts. But beyond that, he's also cautious, and insecure to a degree as he tries to navigate life among creatures beyond what he grew up with. In a way, he lived in a bubble growing up just as Riddle did ( though what that looked like was very different from Riddle's experiences ), and I think that seeing some of that really hits Riddle a certain way, and makes him feel a bit protective. I also think seeing how scared Sebek was when he got kidnapped, and the panic he went through was simultaneously very shocking and touching for him, because it was a big deal to realize how much he cared. Sorry Diasomnia, Riddle's taking the crocodile now lmao.

My unpopular opinion about this character

I don't really have one. I don't think I'm aware enough of any discourse surrounding him.

One thing I wish would happen / had happened with this character in canon.

I want to see him interacting with his family!! They've been mentioned, so let me see them!! I bet he's really cute around his parents and his older siblings considering the way he tends to look up to those who are older ( and who he deems worthy of his respect ). I also want to see him interact with his horse/any of the horses! I want to know if he's good with them or not.

As stated above, I'd also love to see more of a connection between Diasomnia and the culture of the Valley which Yana keeps dropping hints over. That's not so much Sebek as much as Diasomnia in general, but still.

[xaltius]

DeepSeek vs OpenAI: Which Is the Best AI Model?

The world of artificial intelligence is a whirlwind of innovation, with new models and capabilities emerging at a breathtaking pace. Two names consistently at the forefront of this revolution are the established giant OpenAI and the rapidly ascending DeepSeek. Both offer powerful AI models, but they come with different philosophies, strengths, and target audiences. So, the big question for developers, researchers, and businesses in 2025 is: which one is "best"?

The truth is, "best" is subjective and highly dependent on your specific needs. This blog post aims to dissect the offerings of DeepSeek and OpenAI as of May 2025, providing a balanced comparison to help you decide which AI champion might be the right fit for your projects.

Meet the Contenders

DeepSeek AI: Founded in 2023, DeepSeek has quickly made a name for itself, particularly with its strong emphasis on open-source models and impressive performance in coding and mathematical reasoning. Backed by a mission to make advanced AI more accessible and cost-effective, DeepSeek has released a series of models, including:

DeepSeek LLM Series (e.g., V2, V3): General-purpose language models known for efficient architecture (like Mixture-of-Experts or MoE) and competitive performance.

DeepSeek Coder Series (e.g., V1, V2): Specialized models trained extensively on code, demonstrating remarkable capabilities in code generation, completion, and understanding across numerous programming languages.

DeepSeek-R1/R2 (Reasoning Models): Models focusing on advanced reasoning and problem-solving, showing strength in areas like mathematics. DeepSeek-R2, anticipated for early 2025, promises enhanced multilingual reasoning and multimodal capabilities. A key differentiator for DeepSeek is its commitment to open-sourcing many of its foundational models, fostering community development and customization. They also highlight significantly lower training costs compared to some Western counterparts.

OpenAI: A pioneering force in the AI landscape, OpenAI is renowned for its state-of-the-art large language models that have set industry benchmarks. Their flagship models include:

GPT-3.5: A widely used and cost-effective model for a variety of general tasks.

GPT-4: A high-performance model known for its advanced reasoning, creativity, and improved accuracy.

GPT-4o & GPT-4.1 (and variants like mini, nano): OpenAI's latest flagship models as of early-mid 2025, offering enhanced speed, intelligence, and impressive multimodal capabilities (text, image, audio input/output). These models often lead in general-purpose understanding and complex task execution. OpenAI's models are primarily accessed via a proprietary API and their popular ChatGPT interface, focusing on providing polished, powerful, and versatile AI solutions.

Head-to-Head Comparison: Key Areas in 2025

1. Performance & General Benchmarks (MMLU, GSM8K, etc.):

OpenAI (GPT-4.1, GPT-4o): Typically holds the edge in broad general knowledge (MMLU) and diverse reasoning tasks. Their models are trained on vast datasets, giving them a comprehensive understanding across many domains.

DeepSeek (DeepSeek-V3, DeepSeek-R1/R2): Shows incredibly strong, sometimes leading, performance in mathematical reasoning (e.g., MATH-500, AIME benchmarks) and is highly competitive on general benchmarks like MMLU. DeepSeek-V3 has demonstrated scores surpassing or rivaling some GPT-4 variants in specific areas. The gap appears to be narrowing, with DeepSeek models showing remarkable efficiency.

2. Coding Prowess (HumanEval, Codeforces):

DeepSeek Coder Series: This is a standout area for DeepSeek. Trained extensively on code (often 80%+ of their training data for these models), DeepSeek Coder models frequently achieve top-tier results on coding benchmarks like HumanEval, sometimes outperforming generalist models from OpenAI. They support a vast number of programming languages.

OpenAI (GPT-4.1, GPT-4o): Also possess excellent coding capabilities, offering robust code generation, explanation, and debugging. While very strong, their training is more generalist compared to DeepSeek's dedicated coder models.

3. Multimodality (Text, Image, Audio):

OpenAI (GPT-4o, GPT-4.1): Leads significantly in this domain as of early 2025. GPT-4o, for instance, natively processes and generates content across text, image, and audio in real-time, offering a seamless multimodal experience.

DeepSeek: While foundational DeepSeek models were initially text-focused, the upcoming DeepSeek-R2 is slated to introduce robust multimodal functionality (text, image, audio, basic video understanding). DeepSeek-VL models also cater to visual language tasks. However, OpenAI has a more mature and widely accessible multimodal offering currently.

4. Openness & Customization:

DeepSeek: A major advantage for DeepSeek is its open-source approach for many of its models (like DeepSeek-R1, DeepSeek-V3 LLM 7B & 67B, DeepSeek Coder). This allows developers to download, self-host, and fine-tune models for specific needs, offering transparency and control.

OpenAI: Operates on a primarily proprietary, closed-source model. While offering extensive API access and some fine-tuning capabilities, the base models are not open.

5. API, Pricing, and Accessibility:

DeepSeek: Actively promotes cost-effectiveness. Their API pricing is often significantly lower (sometimes cited as 20-50% cheaper or even more for certain token counts) than OpenAI's premium models. They aim for transparent and flexible pricing tiers. Their open-source models can be free to run if self-hosted.

OpenAI: Offers a well-established API with extensive documentation and integrations. While they have free tiers for ChatGPT and some API access, their most powerful models (like GPT-4.1) come with premium pricing.

6. Innovation Trajectory & Recent Developments:

DeepSeek: Rapidly innovating with a focus on efficiency (MoE architecture, FP8 training), cost-reduction in training, and specialized models (coding, reasoning). Their "Open Source Week" initiatives demonstrate a commitment to community and democratization. The development of DeepSeek-R2 signals a push towards advanced multilingual reasoning and broader multimodality.

OpenAI: Continues to push the boundaries of general AI intelligence and multimodal interaction with models like GPT-4o and the developer-focused GPT-4.1 series. They are also focusing on reasoning capabilities with their 'o-series' models and expanding enterprise offerings.

7. Language Support:

DeepSeek: While strong in English and Chinese, with upcoming models like DeepSeek-R2 aiming for broader multilingual reasoning, its current widely-available models might have a stronger focus on these languages compared to OpenAI's extensive multilingual support.

OpenAI: Generally offers robust support for a wider array of languages across its model lineup.

Strengths and Weaknesses Summary

DeepSeek AI:

Pros: Strong (often leading) in coding and mathematical reasoning, cost-effective (especially API and self-hosted open-source models), many open-source options offering transparency and customization, innovative and efficient model architectures.

Cons: Newer ecosystem compared to OpenAI, currently less mature in broad multimodal capabilities (though this is changing), general knowledge base might still be catching up to OpenAI's top-tier models in some niche areas, wider language support still evolving.

OpenAI:

Pros: State-of-the-art general-purpose intelligence and reasoning, leading multimodal capabilities (GPT-4o), mature and well-documented API, vast ecosystem and community, strong performance across a wide range of languages and creative tasks.

Cons: Primarily proprietary and closed-source, can be expensive for high-volume API usage of top models, development of truly open high-performance models is not their primary focus.

So, Which is "Best" in 2025?

As anticipated, there's no single "best." The optimal choice hinges on your specific priorities:

For Developers Prioritizing Open-Source & Top-Tier Coding/Math: If you need highly capable models for coding or mathematical tasks, value the ability to self-host, fine-tune extensively, or require a cost-effective API, DeepSeek's Coder and Reasoning models (like R1/R2) are exceptionally compelling.

For Cutting-Edge Multimodal Applications & General-Purpose Excellence: If your application demands seamless interaction with text, images, and audio, or requires the highest levels of general knowledge and nuanced understanding for diverse tasks, OpenAI's GPT-4o or GPT-4.1 series likely remains the front-runner.

For Budget-Conscious Startups & Researchers: DeepSeek's aggressive pricing and open-source offerings provide an accessible entry point to powerful AI without breaking the bank.

For Enterprise Solutions Requiring Broad Capabilities & Mature Integrations: OpenAI's established platform and versatile models are often favored for broader enterprise deployments, though DeepSeek is making inroads.

The Ever-Evolving AI Landscape

It's crucial to remember that the AI field is in constant flux. Benchmarks are just one part of the story. Real-world performance, ease of integration, specific feature sets, and the cost-benefit analysis for your particular use case should guide your decision. What's cutting-edge today might be standard tomorrow.

The healthy competition between DeepSeek, OpenAI, and other AI labs is fantastic news for all of us, driving innovation, improving accessibility, and continually expanding the horizons of what AI can achieve.

[deepseekagi]

🧠 The Best Open-Source LLM You’ve Never Heard Of?

Say hello to DeepSeek-V3, a monster 671B parameter model that’s shaking the AI world.

🚨 Benchmarks? ✅ MMLU – 88.5% ✅ HumanEval – 82.6% ✅ DROP – 91.6 ✅ MATH-500 – 90.2% ✅ Chinese C-Eval – 86.5%

Built with a Mixture-of-Experts setup, it uses only ~37B params at a time, runs on FP8, and costs just $5.6M to train (yes, that’s cheap). Even Claude and GPT-4 are sweating.

But here’s the kicker: ⚠️ It routes all data to Chinese servers — so privacy? Yeah… that’s the plot twist. 🕵️‍♀️

💾 It's open-source. It’s MIT licensed. It’s blazing fast. 🔗 Full breakdown and spicy comparisons 👉 https://deepseekagi.org/deepseek-v3-architecture/

#DeepSeek #LLM #GPT4 #Claude3 #OpenSource #CyberpunkVibes #TumblrTech #MoE #FP8 #AIWhispers

[new876868767]

[ad_1] Alibaba Group’s newly-released large language model Qwen3 has shown higher mathematical-proving and code-writing abilities than its previous models and some American peers, putting it at the top of benchmark charts. Qwen3 offers two mixture-of-experts (MoE) models (Qwen3-235B-A22B and Qwen3-32B-A3B) and six dense models. A MoE, also used by OpenAI’s ChatGPT and Anthropic’s Claude, can assign a specialized “expert” model to answer questions on a specific topic. A dense model can perform a wide range of tasks, such as image classification and natural language processing, by learning complex patterns in data.Alibaba, a Hangzhou-based company, used 36 trillion tokens to train Qwen3, doubling the number used for training the Qwen2.5 model. DeepSeek, another Hangzhou-based firm, used 14.8 trillion tokens to train its R1 model. The higher the number of tokens used, the more knowledgeable an AI model is.At the same time, Qwen3 has a lower deployment threshold than DeepSeek V3, meaning users can deploy it at lower operating costs and with reduced energy consumption.Qwen3-235B-A22B features 235 billion parameters but requires activating only 22 billion. DeepSeek R1 features 671 billion parameters and requires activating 37 billion. Fewer parameters mean lower operation costs.The US stock market slumped after DeepSeek launched its R1 model on January 20. AI stock investors were shocked by DeepSeek R1’s high performance and low training costs.Media reports said DeepSeek will unveil its R2 model in May. Some AI fans expected DeepSeek R2 to have greater reasoning ability than R1 and the ability to catch up with OpenAI o4-mini. ‘Nonsensical benchmark hacking’Since Alibaba released Qwen3 early on the morning of April 29, AI fans have performed various tests to check its performance.The Yangtze Evening News reported that Qwen3 scored 70.7 on LiveCodeBench v5, which tests AI models’ code-writing ability. This beat DeepSeek R1 (64.3), OpenAI o3-mini (66.3), Gemini2.5 Pro (70.4), and Grok 3 Beta (70.6).On AIME’24, which tests AI models’ mathematical-proofing ability, Qwen3 scored 85.7, better than DeepSeek R1 (79.8), OpenAI o3-mini (79.6), and Grok 3 Beta (83.9). However, it lagged behind Gemini2.5 Pro, which scored 92.The newspaper’s reporter found that Qwen3 fails to deal with complex reasoning tasks and lacks knowledge in some areas, resulting in “hallucinations,” a typical situation in which an AI model provides false information.“We asked Qwen3 to write some stories in Chinese. We feel that the stories are more delicate and fluent than those written by previous AI models, but their flows and scenes are illogical,” the reporter said. “The AI model seems to be putting everything together without thinking.”In terms of scientific reasoning, Qwen3 scored 70%, lagging behind Gemini 2.5 Pro (84%), OpenAI o3-mini (83%), Grok 3 mini (79%), and DeepSeek R1 (71%), according to Artificial Analysis, an independent AI benchmarking & analysis company. In terms of reasoning and knowledge in humanity, Qwen3 scored 11.7%, beating Grok 3 mini (11.1%), Claude 3.7 (10.3%), and DeepSeek R1 (9.3%). However, it still lagged behind OpenAI o3-mini (20%) and Gemini 2.5 Pro (17.1%).In February of this year, Microsoft Chief Executive Satya Nadella said that focusing on self-proclaimed milestones, such as achieving artificial general intelligence (AGI), is only a form of “nonsensical benchmark hacking.” He said an AI model can declare victory only if it helps achieve a 10% annual growth in gross domestic product. Chip shortageWhile Chinese AI firms need more time to catch up with American players, they face a new challenge – a shortage of AI chips.In early April, Chinese media reported that ByteDance, Alibaba, and Tencent reportedly ordered more than 100,000 H20 chips from Nvidia for 16 billion yuan (US$2.2 billion).On April 15, Nvidia said it had been informed by the US government informed that the company would need a license to ship its H20 AI chips to China. The government cited the risk that Chinese firms would use the H20 chips in supercomputers.The Information reported on May 2 that Nvidia had told some of its biggest Chinese customers that it is tweaking the design of its AI chips so they can continue to ship AI chips to China. A sample of the new chip will be available as early as June. Nvidia has already tailored AI chips for the Chinese market several times. After Washington restricted the export of A100 and H100 chips to China in October 2022, Nvidia designed the A800 and H800 chips. However, the US government extended its export controls to cover them in October 2023. Then, Nvidia unveiled the H20.Although the H20 only performs equivalent to 15% of the H100, Chinese firms are still rushing to buy it, instead of Huawei’s Ascend 910B chip, which faces a limited supply due to a low production yield.A Chinese IT columnist said the Ascend 910B is a faster chip than the H20, but the H20’s bandwidth is ten times that of the 910B’s. He said a higher bandwidth in an AI chip, like a better gearbox in a sports car, can achieve a more stable performance.The Application of Electronic Technique, a Chinese scientific journal, said China’s AI firms could try to use homegrown chips, such as Cambricon Technologies’ Siyuan 590, Hygon Information Technology’s DCU series, Moore Threads’ MTT S80, Biren Technology’s BR104, or Huawei’s upcoming Ascend 910C.  Read: After DeepSeek: China’s Manus – the hot new AI under the spotlight [ad_2] Source link

[news786hz]

Alibaba Qwen Team Just Released Qwen3: The Latest Generation of Large Language Models in Qwen Series, Offering a Comprehensive Suite of Dense and Mixture-of-Experts (MoE) Models

Alibaba Qwen Team Just Released Qwen3: The Latest Generation of Large Language Models in Qwen Series, Offering a Comprehensive Suite of Dense and Mixture-of-Experts (MoE) Models

[quickpc]

Qwen3 กลุ่มโมเดลปัญญาประดิษฐ์ (AI) รุ่นใหม่ล่าสุดที่ออกแบบมาเพื่อตอบโจทย์การใช้งานหลากหลาย ตั้งแต่การเขียนโค้ด การแก้ปัญหาคณิตศาสตร์ ไปจนถึงการประมวลผลภาษาธรรมชาติและงานมัลติโมดัล โดย Qwen3 ประกอบด้วย 6 โมเดลแบบ dense (0.6B, 1.7B, 4B, 8B, 14B, 32B) และ 2 โมเดลแบบ Mixture of Experts (MoE) (30B-A3B และ 235B-A22B) ซึ่งเป็นโมเดลเรือธงที่มีประสิทธิภาพสูงสุด โมเดลนี้รองรับ 119 ภาษา รวมถึงภาษาไทย และมีความสามารถในการสลับโหมดการทำงานระหว่าง “โหมดคิด” (think mode) และ “โหมดไม่คิด” เพื่อปรับการประมวลผลให้เหมาะสมกับงานที่แตกต่างกัน นอกจากนี้ Qwen3 ยังใช้สถาปัตยกรรม Mixture of Experts (MoE) ในบางโมเดล ซึ่งช่วยเพิ่มประสิทธิภาพการคำนวณโดยแบ่งงานย่อยให้โมเดลผู้เชี่ยวชาญเฉพาะด้านจัดการ

[diabetickart]

Mixture-of-Experts: The Architecture Behind Smarter AI

Future scalable AI systems are being shaped by Mixture-of-Experts (MoE) models. MoE routes tasks selectively rather than activating every neuron, improving efficiency and performance. This clever architecture is used by DeepSeek-V3-0324 to balance cost and power; it has 671 billion parameters, but only activates 37 billion each token. For practical activities like writing, thinking, and coding, this makes it an excellent option. The most recent model from DeepSeek is a must-see if you're curious about how MoE models are advancing AI. Go here to find out more about DeepSeek-V3-0324.

[komalrajput3]

A Technical and Business Perspective for Choosing the Right LLM for Enterprise Applications.

In 2025, Large Language Models (LLMs) have emerged as pivotal assets for enterprise digital transformation, powering over 65% of AI-driven initiatives. From automating customer support to enhancing content generation and decision-making processes, LLMs have become indispensable. Yet, despite the widespread adoption, approximately 46% of AI proofs-of-concept were abandoned and 42% of enterprise AI projects were discontinued, mainly due to challenges around cost, data privacy, and security. A recurring pattern identified is the lack of due diligence in selecting the right LLM tailored to specific enterprise needs. Many organizations adopt popular models without evaluating critical factors such as model architecture, operational feasibility, data protection, and long-term costs. Enterprises that invested time in technically aligning LLMs with their business workflows, however, have reported significant outcomes — including a 40% drop in operational costs and up to a 35% boost in process efficiency.

LLMs are rooted in the Transformer architecture, which revolutionized NLP through self-attention mechanisms and parallel processing capabilities. Components such as Multi-Head Self-Attention (MHSA), Feedforward Neural Networks (FFNs), and advanced positional encoding methods (like RoPE and Alibi) are essential to how LLMs understand and generate language. In 2025, newer innovations such as FlashAttention-2 and Sparse Attention have improved speed and memory efficiency, while the adoption of Mixture of Experts (MoE) and Conditional Computation has optimized performance for complex tasks. Tokenization techniques like BPE, Unigram LM, and DeepSeek Adaptive Tokenization help break down language into machine-understandable tokens. Training strategies have also evolved. While unsupervised pretraining using Causal Language Modeling and Masked Language Modeling remains fundamental, newer approaches like Progressive Layer Training and Synthetic Data Augmentation are gaining momentum. Fine-tuning has become more cost-efficient with Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA, QLoRA, and Prefix-Tuning. Additionally, Reinforcement Learning with Human Feedback (RLHF) is now complemented by Direct Preference Optimization (DPO) and Contrastive RLHF to better align model behavior with human intent.

From a deployment perspective, efficient inference is crucial. Enterprises are adopting quantization techniques like GPTQ and SmoothQuant, as well as memory-saving architectures like xFormers, to manage computational loads at scale. Sparse computation and Gated Experts further enhance processing by activating only the most relevant neural pathways. Retrieval-Augmented Generation (RAG) has enabled LLMs to respond in real-time with context-aware insights by integrating external knowledge sources. Meanwhile, the industry focus on data security and privacy has intensified. Technologies like Federated Learning, Differential Privacy, and Secure Multi-Party Computation (SMPC) are becoming essential for protecting sensitive information. Enterprises are increasingly weighing the pros and cons of cloud-based vs. on-prem LLMs. While cloud LLMs like GPT-5 and Gemini Ultra 2 offer scalability and multimodal capabilities, they pose higher privacy risks. On-prem models like Llama 3, Falcon 2, and DeepSeek ensure greater data sovereignty, making them ideal for sensitive and regulated sectors.

Comparative evaluations show that different LLMs shine in different use cases. GPT-5 excels in customer service and complex document processing, while Claude 3 offers superior ethics and privacy alignment. DeepSeek and Llama 3 are well-suited for multilingual tasks and on-premise deployment, respectively. Models like Custom ai Gemini Ultra 2 and DeepSeek-Vision demonstrate impressive multimodal capabilities, making them suitable for industries needing text, image, and video processing. With careful evaluation of technical and operational parameters — such as accuracy, inference cost, deployment strategy, scalability, and compliance — enterprises can strategically choose the right LLM that fits their business needs. A one-size-fits-all approach does not work in LLM adoption. Organizations must align model capabilities with their core objectives and regulatory requirements to fully unlock the transformative power of LLMs in 2025 and beyond.

[xaltius]

DeepSeek vs. ChatGPT: How Do They Compare?

In the rapidly evolving landscape of artificial intelligence, two names frequently emerge in discussions about cutting-edge language models: DeepSeek and ChatGPT. Both are powerful tools capable of generating human-like text, answering questions, and assisting with a wide range of tasks. However, they have distinct architectures, strengths, and philosophies that set them apart. Let's delve into a comparison of these AI titans.

DeepSeek: The Efficient and Open Challenger

DeepSeek, developed by the Chinese AI company of the same name, has garnered attention for its impressive performance, particularly in technical domains, while boasting remarkable efficiency and an open approach.

Key Characteristics of DeepSeek:

Architecture: DeepSeek models often utilize a Mixture-of-Experts (MoE) architecture. This means that the model has a vast number of parameters (knowledge points), but only a small subset of these are activated for any given task. This selective activation leads to efficient resource use and potentially faster processing for specific queries.

Strengths: DeepSeek has demonstrated strong capabilities in areas like coding, mathematics, and logical reasoning. It often excels in tasks requiring structured and technical understanding. Its ability to handle long context windows (up to 128K tokens in some models) is also a significant advantage for processing extensive information.

Cost-Efficiency: A significant highlight of DeepSeek is its cost-effectiveness. Its architecture and training methods often result in lower computational costs compared to models with fully activated parameters. Furthermore, DeepSeek adopts an open-source approach, making its models freely available for download, use, and modification, which can be a considerable advantage for developers and organizations with budget constraints.

Accessibility: DeepSeek's commitment to open-source principles makes advanced AI technology more accessible to a wider audience, fostering collaboration and innovation within the AI community.

Potential Limitations: While strong in technical domains, some sources suggest that DeepSeek's performance might slightly dip in more casual, nuanced conversations or when dealing with a broader range of languages compared to ChatGPT. Its user interface and integration might also be less polished out-of-the-box, potentially requiring more technical expertise for deployment and customization.

Example: DeepSeek might be particularly adept at generating efficient code snippets for specific algorithms or solving complex mathematical problems step-by-step.

ChatGPT: The Versatile and User-Friendly Maestro

ChatGPT, developed by OpenAI, is arguably the more widely recognized name, known for its versatility, user-friendliness, and strong performance across a broad spectrum of topics.

Key Characteristics of ChatGPT:

Architecture: ChatGPT is based on OpenAI's Generative Pre-trained Transformer (GPT) series of large language models. These models utilize a transformer architecture that excels at understanding context and generating coherent, human-like text.

Strengths: ChatGPT shines in its ability to handle a wide range of topics with impressive flexibility. It excels at understanding context, generating creative content, engaging in natural-sounding conversations, and providing clear explanations. Its multilingual capabilities are also a strong point, offering consistent performance across various languages.

User Experience: ChatGPT boasts a polished and accessible user interface through its web and mobile applications. It offers features like conversation history, organization tools, and even supports multimodal inputs (text, images, voice in some tiers). Its well-documented API also simplifies integration for developers.

Ecosystem and Features: ChatGPT has a rich ecosystem of features and integrations, including plugins for web browsing, code interpretation, and connections to various third-party applications. Features like custom instructions, memory, and the ability to create custom GPTs enhance its usability and adaptability.

Pricing Model: ChatGPT follows a tiered pricing model, with a free tier offering access to older models with limitations, and paid subscriptions unlocking more advanced models (like GPT-4o), higher usage limits, and additional features. Enterprise solutions with custom features are also available.

Potential Limitations: While highly capable, ChatGPT's reliance on a fully activated parameter model can be computationally intensive, potentially leading to higher costs for extensive use through its API. Its open-ended conversational nature might sometimes lead to less focused or slightly less precise responses compared to DeepSeek in specific technical domains.

Example: ChatGPT could be excellent at brainstorming creative writing prompts, explaining complex concepts in simple terms, or drafting marketing copy in multiple languages.

Head-to-Head: Key Differences Summarized

Which One Should You Choose?

The "better" model ultimately depends on your specific needs and priorities:

Choose DeepSeek if:

Your primary focus is on technical tasks like coding, mathematics, and data analysis.

Cost-efficiency is a major concern.

You require processing of very long documents or codebases.

You prefer the flexibility and control of an open-source model and have the technical expertise for implementation.

Choose ChatGPT if:

You need a versatile AI assistant for a wide range of tasks, including creative writing, general knowledge queries, and conversational applications.

User-friendliness and ease of use are paramount.

Strong multilingual support is required.

You value a rich ecosystem of features and integrations.

You are comfortable with a subscription-based pricing model for advanced features.

The Future of AI Language Models

Both DeepSeek and ChatGPT represent significant advancements in AI language models. DeepSeek's focus on efficiency and open access is pushing the boundaries of how powerful AI can be made more accessible. ChatGPT's emphasis on user experience and broad capabilities has made AI a more integrated part of everyday workflows for many.

As the field continues to evolve, we can expect further innovations in both efficiency and versatility, potentially blurring the lines between these current leaders. The competition between models like DeepSeek and ChatGPT ultimately benefits users by driving progress and offering a wider range of powerful AI tools to choose from.

[thecodinglove]

Meta suspected of cheating to boost its new Llama 4 model in AI benchmarks

Caught red-handed? — The launch of Meta’s new Llama 4 AI model family this past weekend made quite a splash in tech circles. Touted as heavyweights in artificial intelligence, the Scout and Maverick models from the Llama 4 family were announced as the first to feature the Mixture of Experts (MoE) architecture — a design technique that boosts model power while reducing the resources needed per…

[techinsightnow]

Meta’s surprise Llama 4 drop exposes the gap between AI ambition and reality

Meta constructed the Llama 4 models using a mixture-of-experts (MoE) architecture, which is one way around the limitations of running huge AI models. Think of MoE like having a large team of specialized workers; instead of everyone working on every task, only the relevant specialists activate for a specific job. For example, Llama 4 Maverick features a 400 billion parameter size, but only 17…

[govindhtech]

Ironwood AI Chip: Google’s 7th-gen Tensor Processing Unit

Ironwood, Google's seventh-generation Tensor Processing Unit (TPU), is the first AI accelerator designed for inference and the most scalable and performant.

Google's most difficult AI training and serving workloads have used TPUs for over 10 years, and Cloud customers can too. Ironwood is the most powerful, competent, and energy-efficient TPU. It is also built for large-scale inferential AI models.

Ironwood is a major milestone in AI and its framework. AI models that produce insights and interpret themselves are replacing responsive models that provide real-time information for human interpretation. In the “age of inference,” AI agents will proactively produce and retrieve data to collaboratively deliver answers and insights, not just facts.

Ironwood can manage the huge computational and communication needs of next-generation generative AI. It can support 9,216 liquid-cooled chips linked by 10 MW of revolutionary Inter-Chip Interconnect (ICI) networking. It is one of several unique features of the Google Cloud AI Hypercomputer architecture, which optimises software and hardware for complicated AI tasks. By using Google's Pathways software stack, Ironwood AI Chip lets developers easily and consistently use tens of thousands of Ironwood TPUs.

Ironwood powers the inference age

Ironwood AI Chip handles the complex communication and computation needs of "thinking models," including complex reasoning tasks, Mixture of Experts (MoEs), and Large Language Models. These models require efficient memory access and massive parallel computing. Ironwood is designed to manipulate massive tensors with little data flow and latency. Frontier thinking models demand more processing than a single chip can manage. Google developed Ironwood TPUs with a high-bandwidth, low-latency ICI network for TPU pod-scale synchronous, coordinated communication.

Ironwood comes in 256 and 9,216 chip variants for Google Cloud customers based on AI workloads.

El Capitan, the world's largest supercomputer, delivers 1.7 Exaflops per pod when scaled to 9,216 processors per pod, or 42.5 Exaflops in total. Ironwood can handle 24 times that. Ironwood AI Chip delivers massive parallel computing capacity for the hardest AI tasks, such as ultra-dense LLM or MoE models with thinking skills for training and inference. Each chip is outstanding with 4,614 TFLOPs peak computing. AI technology has advanced greatly. Ironwood's memory and network design ensures data availability for optimal performance at this massive scale.

Ironwood also contains an upgraded SparseCore, an accelerator for ultra-large embeddings common in advanced ranking and recommendation workloads. Ironwood's increased SparseCore support speeds up scientific, financial, and AI workloads.

Google DeepMind's Pathways machine learning software enables distributed processing across several TPU devices. Google Cloud's Pathways make it easy to construct hundreds of thousands of Ironwood AI Chips to swiftly test AI computation limits.

Key Ironwood characteristics

Google Cloud, which integrates AI computing into Gmail, Search, and other services for billions of users everyday, is the only hyperscaler with over 10 years of experience enabling cutting-edge research. Ironwood AI Chip capabilities are based on this experience. Notable traits include:

AI applications are more cost-effective due to performance advancements and power efficiency. Ironwood has double the performance per watt of Trillium, a sixth-generation TPU introduced last year. The offer more capacity per watt for client workloads when power availability limits AI capabilities. Advanced liquid cooling methods and optimised chip design can retain up to double the performance of traditional air cooling under continual, heavy AI workloads. Compared to the 2018 Cloud TPU, Ironwood AI Chip is 30 times more power efficient.

High Bandwidth Memory capacity increases significantly. Ironwood can analyse bigger models and information with 192 GB per chip, six times more than Trillium, reducing data transfers and improving speed.

HBM bandwidth was 4.5 times Trillium and reached 7.2 TBps per chip. For modern AI's memory-intensive operations, this high bandwidth ensures fast data access.

Improved Inter-Chip Interconnect bandwidth. This is now 1.2 Tbps bidirectional, 1.5x faster than Trillium, enabling faster chip-to-chip communication and scaled distributed training and inference.

Ironwood meets tomorrow's AI needs

In the era of inference, Ironwood AI Chip's processing power, memory capacity, ICI networking advances, and dependability are revolutionary. With these advances and a nearly twofold boost in power efficiency, the most demanding clients can now train and serve workloads with the greatest performance and lowest latency while meeting exponential computing demand. Modern models like Gemini 2.5 and the Nobel Prize-winning AlphaFold employ TPUs. When Ironwood launches later this year, we're enthusiastic to see what AI advancements developers and Google Cloud customers will create.