#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…

[daviddavi09]

Mixture of Experts Explained – The Brain Behind Modern AI

In this video, Ansh explains one of the most thrilling developments in contemporary AI architecture — the Mixture of Experts (MoE). As AI models have expanded to trillions of parameters, MoE provides a more intelligent, more efficient method of employing these gigantic networks by only allowing a few expert sub-networks to be turned on for each task. Anch describes how MoE operates, why it's a performance and scalability game-changer, and sheds light on real applications such as Google's Switch Transformer, G-Shard, Microsoft's DeepSpeed MoE, and even its potential use in GPT-4. He gets technical with gating networks, sparse activation, and token-level routing, as well as discusses issues such as load balancing and training stability. Anch concludes with a passionate interpretation of the future of AI: smart strategy over dumb power, and the need for open-access to this powerful technology. As a developer, researcher, or simply AI-interested, this is a must-watch deconstruction of the mind behind current artificial intelligence.

#mixtureofexperts #aiarchitecture #machinelearning #deeplearning #transformers #sparsemodels #gshard #switchtransformer #deeplearningexplained #openai #gpt4 #futureofai #scalableai #techbreakdown #aiexplained #anchtech #neuralnetworks #efficientai #aiinnovation #moemodels

[w2gsolution01]

MiniMax Unveils M1: A 456B Hybrid Model for Extended Reasoning and Software Solutions

The artificial intelligence landscape is evolving rapidly, and MiniMax Unveils M1, a groundbreaking development that promises to redefine efficiency and capability in AI reasoning. This 456-billion-parameter model, built with a hybrid Mixture-of-Experts (MoE) architecture and a novel Lightning Attention mechanism, is designed to tackle complex, long-context tasks while keeping computational costs remarkably low. Released under an open-source Apache 2.0 license, this model is poised to empower developers, researchers, and businesses with unprecedented access to advanced AI tools.

What Makes the M1 Model Unique?

MiniMax, a Shanghai-based AI startup, has crafted the M1 to stand out in a crowded field of large language models. Unlike many competitors, this model combines efficiency with power, making it a game-changer for industries requiring robust reasoning and software development capabilities.

A Massive 1-Million-Token Context Window

One of the standout features of the M1 is its ability to process up to 1 million input tokens and generate up to 80,000 output tokens. This expansive context window allows the model to handle vast amounts of data—equivalent to processing an entire book or a large codebase in a single interaction. For comparison, many leading models, such as OpenAI’s GPT-4o, are limited to much smaller context windows, making M1 a leader in long-context reasoning.

This capability is particularly valuable for applications like document analysis, where understanding intricate relationships across lengthy texts is critical. Developers can leverage this feature to create tools that summarize complex reports, analyze legal documents, or even generate detailed narratives without losing track of context.

Hybrid Mixture-of-Experts Architecture

The M1’s architecture is a blend of innovation and efficiency. By using a Mixture-of-Experts (MoE) approach, the model activates only a fraction—approximately 45.9 billion—of its 456 billion parameters per token. This selective activation reduces computational demands, allowing the model to perform complex tasks with significantly less power than traditional models.

The inclusion of the Lightning Attention mechanism further enhances efficiency. This novel approach combines linear attention for long sequences with periodic Softmax attention for expressive power, enabling the model to process data at a fraction of the computational cost of competitors like DeepSeek’s R1. According to MiniMax, M1 uses just 25% of the floating-point operations (FLOPs) required by DeepSeek R1 at a 100,000-token generation length.

Unmatched Efficiency in Training

MiniMax Unveils M1 as a model that redefines cost-effectiveness in AI development. The model was trained using a reinforcement learning (RL) approach powered by a custom algorithm called CISPO (Clipped Importance Sampling Policy Optimization). This algorithm optimizes training by clipping importance sampling weights, resulting in greater stability and efficiency.

A Budget-Friendly Breakthrough

Remarkably, MiniMax reports that the M1 was fine-tuned on 512 H800 GPUs over just three weeks, with a total training cost of approximately $534,700. This figure is a fraction of the budgets typically required for models of comparable scale—OpenAI’s GPT-4 training, for instance, is estimated to have cost over $100 million. This cost-efficiency could democratize access to advanced AI, enabling smaller organizations to compete with industry giants.

CISPO: The Secret to Scalability

The CISPO algorithm is a key differentiator, doubling the efficiency of reinforcement learning fine-tuning. By focusing on importance sampling weights rather than token updates, CISPO reduces computational overhead while maintaining high performance. This innovation allows M1 to excel in tasks requiring multi-step reasoning, such as mathematical problem-solving and software engineering.

Performance That Rivals Industry Leaders

MiniMax Unveils M1 as a model that competes with top-tier proprietary models from companies like OpenAI, Anthropic, and Google DeepMind. Third-party benchmarks, including AIME 2024, LiveCodeBench, and SWE-bench Verified, show that M1 performs on par with or surpasses models like Google’s Gemini 2.5 Pro and DeepSeek’s R1 in tasks like coding, math, and domain-specific knowledge.

Excelling in Software Development

For developers, M1’s capabilities in software engineering are particularly compelling. The model scores competitively on benchmarks like SWE-bench, with the M1-80K variant achieving a 56.0% success rate, slightly below DeepSeek R1’s 57.6% but significantly ahead of other open-source models. This makes M1 an ideal choice for building internal copilots, automating code reviews, or developing complex software solutions.

Long-Context Reasoning for Real-World Applications

The ability to handle extended contexts makes M1 a versatile tool for real-world applications. From analyzing lengthy research papers to generating detailed technical documentation, the model’s 1-million-token context window ensures it can maintain coherence and accuracy over vast datasets. This capability is especially valuable in industries like finance, legal, and healthcare, where processing large volumes of text is a daily requirement.

Open-Source Accessibility

By releasing M1 under the Apache 2.0 license, MiniMax has made its weights and technical reports freely available on platforms like Hugging Face and GitHub. This open-source approach sets M1 apart from models like Meta’s Llama, which operates under restrictive community licenses, or DeepSeek’s partially open-source offerings.

Empowering Developers and Researchers

The open-source nature of M1 allows developers and researchers to inspect, modify, and build upon the model. This accessibility fosters innovation, enabling teams to fine-tune M1 for specific use cases or integrate it into existing AI pipelines using tools like vLLM or Transformers. The model’s efficiency also translates to lower operational costs, making it an attractive option for startups and academic institutions.

Community-Driven Innovation

MiniMax’s decision to open-source M1 has sparked excitement in the AI community. Posts on X highlight the model’s 1-million-token context window and cost-effective training as major milestones, with developers eager to test its capabilities in real-world scenarios. This community enthusiasm underscores M1’s potential to drive collaborative advancements in AI.

Industry Implications and Future Potential

MiniMax Unveils M1 at a time when the AI industry is grappling with high costs and computational demands. The model’s efficiency and performance could disrupt the market, challenging the dominance of proprietary models and encouraging a shift toward open-source solutions.

A Competitive Edge for Enterprises

For businesses, M1 offers a cost-effective alternative to expensive proprietary models. Its ability to handle complex tasks with minimal resources makes it ideal for enterprises looking to scale AI capabilities without breaking the bank. Whether it’s automating customer service, optimizing supply chains, or developing AI-driven software, M1 provides a flexible and powerful solution.

Reshaping the AI Landscape

The release of M1 signals a broader trend in AI development: a focus on efficiency and accessibility. As MiniMax continues its “MiniMaxWeek” campaign, with additional announcements expected, the company is positioning itself as a leader in the global AI race. Backed by industry giants like Alibaba and Tencent, MiniMax is well-equipped to drive further innovation.

Challenges and Considerations

While M1’s claims are impressive, industry analysts urge caution. The model’s performance metrics, while promising, require independent verification to confirm their accuracy. Additionally, integrating a model of this scale into existing infrastructure may pose challenges for organizations without significant technical expertise.

The Need for Verification

Some experts note that MiniMax’s reported training costs and performance benchmarks need further scrutiny. While the $534,700 training budget is remarkable, it reflects fine-tuning rather than training from scratch, which may explain the lower cost. Independent testing will be crucial to validate M1’s capabilities against competitors.

Scalability for Smaller Teams

For smaller teams, deploying M1 may require investment in compatible hardware and software frameworks. However, the model’s compatibility with tools like vLLM and its open-source availability mitigate these challenges, offering a clear path for adoption.

Conclusion

MiniMax Unveils M1 as a transformative force in AI, blending cutting-edge technology with unparalleled efficiency. Its 1-million-token context window, hybrid MoE architecture, and cost-effective training make it a standout choice for developers, researchers, and businesses. As an open-source model, M1 invites collaboration and innovation, promising to reshape how we approach complex reasoning and software development. With MiniMax leading the charge, the future of AI looks more accessible, efficient, and powerful than ever.

[investmentresearch]

The AI Disruption No One Saw Coming

DeepSeek Disruption: Changing the Tech & Semiconductor Landscape

In 2025, DeepSeek, a Chinese AI startup, introduced the DeepSeek-R1 model—an open-source AI outperforming OpenAI's o1 at a fraction of the cost. This innovation is shaking up global AI and semiconductor markets.

🔍 Why DeepSeek Matters

Reasoning AI: DeepSeek-R1 uses reinforcement learning for advanced problem-solving, unlike traditional language models. Source: DeepSeek Insights.

Cost Efficiency: By leveraging A100 and H800 GPUs, DeepSeek minimizes hardware costs without sacrificing performance.

Disrupting the Industry: DeepSeek’s Multi-Token Prediction (MTP) and Mixture-of-Experts (MoE) architecture are setting a new industry standard.

"As noted in Leanrs' detailed analysis, DeepSeek is positioned to disrupt AI and semiconductor industries. Read the full article."

For deeper insights, visit: 👉 https://www.leanrs.com/

[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

[aiturtlesai]

Qwen3: The New Frontier of Language Models by Alibaba

  Alibaba has introduced Qwen3, a new generation of large language models that combine dense architectures and Mixture-of-Experts (MoE). These models introduce hybrid modes of thinking, support 119 languages, and offer advanced reasoning and integration with external tools. Key Points: Qwen3-235B-A22B is the flagship model with 235 billion total parameters and 22 billion active parameters. Qwen3-30B-A3B, a smaller MoE model, outperforms larger models in specific benchmarks. Hybrid modes of thinking enable... read more: https://www.turtlesai.com/en/pages-2734/qwen3-the-new-frontier-of-language-models-by-alibaba

[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

[daviddavi09]

Mixture of Experts Explained – The Brain Behind Modern AI

In this video, Ansh explains one of the most thrilling developments in contemporary AI architecture — the Mixture of Experts (MoE). As AI models have expanded to trillions of parameters, MoE provides a more intelligent, more efficient method of employing these gigantic networks by only allowing a few expert sub-networks to be turned on for each task. Anch describes how MoE operates, why it's a performance and scalability game-changer, and sheds light on real applications such as Google's Switch Transformer, G-Shard, Microsoft's DeepSpeed MoE, and even its potential use in GPT-4. He gets technical with gating networks, sparse activation, and token-level routing, as well as discusses issues such as load balancing and training stability. Anch concludes with a passionate interpretation of the future of AI: smart strategy over dumb power, and the need for open-access to this powerful technology. As a developer, researcher, or simply AI-interested, this is a must-watch deconstruction of the mind behind current artificial intelligence.

#mixtureofexperts #aiarchitecture #machinelearning #deeplearning #transformers #sparsemodels #gshard #switchtransformer #deeplearningexplained #openai #gpt4 #futureofai #scalableai #techbreakdown #aiexplained #anchtech #neuralnetworks #efficientai #aiinnovation #moemodels

[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.

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Best Opensource Coding Ai

AI has become the talk of the town nowadays, right? There are tons of AI tools available for different tasks, and new advancements are coming up daily like vibe coding. But how do you actually do vibe coding? Or how do you try out these models? You could use tools like ChatGPT or Claude, but they come with restrictions, and you often need to pay to access full features.

What if you don’t want your data to become part of their training models? That’s where open source coding models come in. You can try them locally, without the internet, and you don’t have to pay for them.

Let’s see Top 7 Open Source AI Coding Tools and how you can try them out.

What Does Open Source AI Coding Tools Mean?

Open source AI coding tools are AI-powered tools (often based on machine learning or LLMs) that help you write, understand, or debug code - and whose source code and/or models are freely available for anyone to use, study, modify, and share.

What Do These AI Tools Usually Do?

They can help with:

Code completion

Code generation (write code from natural language prompts)

Explaining code

Refactoring or optimizing code

Generating tests or documentation

Code search and navigation

Problems with Proprietary AI Coding Tools

Proprietary AI coding tools like ChatGPT, Claude, Cursor, or GitHub Copilot come with several limitations:

1. Paid & Limited Access

Most proprietary AI coding tools are not fully free. They offer limited usage, and to unlock full capabilities, you need to purchase a subscription or plan.

2. Data Privacy Concerns

When using these tools, your code may be sent to the cloud, which can pose privacy and security risks, especially for companies working with sensitive data. In some cases, your code might even be used to further train these AI models.

3. Other Limitations

Limited customization

Vendor lock-in

Licensing risks

To avoid these issues, many developers prefer using open source coding AI models.

Why should try Open Source Coding Tools

Open source coding tools like Deepseek-coder-v2, code llama, qwen2.5-coder offer several benefits:

Transparency: You can access model weights and training data, allowing you to run the models locally or fine-tune them as needed.

Publicly available source code: For many open source models, the backend code, UI, and even editor plugins (like for VS Code) are open for inspection and modification.

Open licensing: These tools are often released under open source licenses like MIT or Apache 2.0, giving you freedom to use and adapt them without legal restrictions.

Top 7 Best Open Source AI Coding Models

Qwen2.5-Coder is the code version of Qwen2.5 . A large language model series by the Qwen team from Alibaba Cloud. It supports code generation, code reasoning, and code fixing. There are six different model sizes: 0.5B, 1.5B, 3B, 7B, 14B, and 32B.

How to use it? If you have Ollama installed, you can run this model with one command:ollama run qwen2.5-coder:0.5

To know more Qwen2.5-coder: https://github.com/QwenLM/Qwen2.5-Coder

CodeLlama

CodeLlama is an AI model built specifically for generating and discussing code. As the name implies, it is developed on top of the LLaMA model (by Meta). It supports many programming languages such as Python, Java, C++, PHP, TypeScript (JavaScript), C#, Bash, and more.

Available sizes: 7B, 13B, 34B, 70B (B = billion parameters).

Run the 7b model using:ollama run codellama:7b

To know more: https://ai.meta.com/blog/code-llama-large-language-model-coding/

DeepSeek-Coder-V2

A few months back, DeepSeek gained a lot of attention as a strong competitor in the AI space. It provides different models for different use cases — including one specifically for coding tasks. DeepSeek-Coder-V2 is an open-source Mixture of Experts (MoE) model that delivers performance comparable to GPT-4 Turbo.

Note: MoE is a technique in machine learning that combines multiple expert models into a larger, more powerful one.

Available sizes: 16B and 236B

Run the 16B model using:ollama run deepseek-coder-v2:16b

To know more about: https://github.com/deepseek-ai/DeepSeek-Coder-V2

ollama run codegemma:2b

To know more about: https://ai.google.dev/gemma/docs/codegemma

ollama run codestral:22b

ollama run granite-code:3b

ollama run starcoder2:3b

Risks That Come with All AI Coding Assistants

Regardless of being open or closed source, there are two major concerns when using AI coding models:

1. Security Issues

Most LLMs have knowledge cutoff dates. In the fast-paced world of open source, vulnerabilities get fixed quickly but AI models may not be aware and could generate vulnerable code.

2. Inaccurate Code and Tests

Sometimes, these models generate code that looks correct but is actually buggy, missing edge cases or best practices. Not only for code—if you ask these coding assistants to generate tests, they often can’t write test cases based on your app logic. Plus, you still need to do a lot of copy-pasting.

How Do We Solve Inaccurate Code Test Issues?

Without using ChatGPT or Claude, how can we efficiently generate test cases for your app without doing copy-paste? That’s where Keploy comes in. You can install the Keploy VSCode extension to create unit tests for your app or you can also get it from the GitHub Marketplace.

Does Keploy Actually Use LLMs?

Yes, Keploy uses LLMs for generating unit tests. Let’s say you need unit test cases for your code instead of writing them manually, you can use Keploy to generate the tests for you. In the backend, Keploy uses multiple LLMs with multiple iterations to create possible unit test cases for your code.

To know more about Keploy: https://keploy.io

Conclusion

Open source AI coding tools are a great alternative to proprietary ones, offering transparency, flexibility, and control. The main advantage of using open source AI models is that you can run them locally. In the previous sections, you saw screenshots of how we ran the Qwen models locally without using the internet so your prompts and data never leave your machine. How cool is that, right?

Also, you don’t need to pay to use these models you can run them anytime and still get great responses. If you're interested, you can even fine-tune the model. Not only can you use Qwen models, but you can also run any of the open source AI models listed above locally with just one command. That’s the superpower of open source AI models compared to proprietary ones.

If needed, you can also use Keploy to write test cases for the code generated by these AI models.

FAQ’s

1. What’s the difference between a general-purpose AI model and a coding-specific AI model?

General-purpose models (like GPT-4) perform a wide range of tasks writing poems, solving math problems, answering questions, etc. Coding-specific models are trained exclusively for programming tasks and perform much better for software development.

2. Can I fine-tune these open source AI models on my own codebase?

Yes! You can fine-tune or retrain them using your own data which is not possible with most closed-source tools.

3. Can I run these models locally?

Yes! You can use Ollama to run these models locally

4. Does Keploy change my app logic?

No. Keploy doesn’t touch your app logic. It only generates test cases automatically by analyzing your code.

5. Can Keploy fix bugs in my code?

No. Keploy won’t fix your bugs it just writes test cases that detect bugs and ensure your app’s behavior remains correct.

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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.