{"id":30023,"date":"2025-08-07T05:57:57","date_gmt":"2025-08-07T05:57:57","guid":{"rendered":"https:\/\/youzum.net\/moe-architecture-comparison-qwen3-30b-a3b-vs-gpt-oss-20b\/"},"modified":"2025-08-07T05:57:57","modified_gmt":"2025-08-07T05:57:57","slug":"moe-architecture-comparison-qwen3-30b-a3b-vs-gpt-oss-20b","status":"publish","type":"post","link":"https:\/\/youzum.net\/de\/moe-architecture-comparison-qwen3-30b-a3b-vs-gpt-oss-20b\/","title":{"rendered":"MoE Architecture Comparison: Qwen3 30B-A3B vs. GPT-OSS 20B"},"content":{"rendered":"<p>This article provides a technical comparison between two recently released Mixture-of-Experts (MoE) transformer models: Alibaba\u2019s Qwen3 30B-A3B (released April 2025) and OpenAI\u2019s GPT-OSS 20B (released August 2025). Both models represent distinct approaches to MoE architecture design, balancing computational efficiency with performance across different deployment scenarios.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Model Overview<\/strong><\/h2>\n<figure class=\"wp-block-table\">\n<table class=\"has-fixed-layout\">\n<thead>\n<tr>\n<th>Feature<\/th>\n<th>Qwen3 30B-A3B<\/th>\n<th>GPT-OSS 20B<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Total Parameters<\/strong><\/td>\n<td>30.5B<\/td>\n<td>21B<\/td>\n<\/tr>\n<tr>\n<td><strong>Active Parameters<\/strong><\/td>\n<td>3.3B<\/td>\n<td>3.6B<\/td>\n<\/tr>\n<tr>\n<td><strong>Number of Layers<\/strong><\/td>\n<td>48<\/td>\n<td>24<\/td>\n<\/tr>\n<tr>\n<td><strong>MoE Experts<\/strong><\/td>\n<td>128 (8 active)<\/td>\n<td>32 (4 active)<\/td>\n<\/tr>\n<tr>\n<td><strong>Attention Architecture<\/strong><\/td>\n<td>Grouped Query Attention<\/td>\n<td>Grouped Multi-Query Attention<\/td>\n<\/tr>\n<tr>\n<td><strong>Query\/Key-Value Heads<\/strong><\/td>\n<td>32Q \/ 4KV<\/td>\n<td>64Q \/ 8KV<\/td>\n<\/tr>\n<tr>\n<td><strong>Context Window<\/strong><\/td>\n<td>32,768 (ext. 262,144)<\/td>\n<td>128,000<\/td>\n<\/tr>\n<tr>\n<td><strong>Vocabulary Size<\/strong><\/td>\n<td>151,936<\/td>\n<td>o200k_harmony (~200k)<\/td>\n<\/tr>\n<tr>\n<td><strong>Quantization<\/strong><\/td>\n<td>Standard precision<\/td>\n<td>Native MXFP4<\/td>\n<\/tr>\n<tr>\n<td><strong>Release Date<\/strong><\/td>\n<td>April 2025<\/td>\n<td>August 2025<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p><em>Sources: <a href=\"https:\/\/qwenlm.github.io\/blog\/qwen3\/\">Qwen3 Official Documentation<\/a>, <a href=\"https:\/\/openai.com\/index\/introducing-gpt-oss\/\">OpenAI GPT-OSS Documentation<\/a><\/em><\/p>\n<h2 class=\"wp-block-heading\"><strong>Qwen3 30B-A3B Technical Specifications<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Architecture Details<\/strong><\/h3>\n<p>Qwen3 30B-A3B employs a deep transformer architecture with <strong>48 layers<\/strong>, each containing a Mixture-of-Experts configuration with <strong>128 experts per layer<\/strong>. The model activates <strong>8 experts per token<\/strong> during inference, achieving a balance between specialization and computational efficiency.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Attention Mechanism<\/strong><\/h3>\n<p>The model utilizes <strong>Grouped Query Attention (GQA)<\/strong> with <strong>32 query heads and 4 key-value heads<\/strong>\u00b3. This design optimizes memory usage while maintaining attention quality, particularly beneficial for long-context processing.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Context and Multilingual Support<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Native context length<\/strong>: 32,768 tokens<\/li>\n<li><strong>Extended context<\/strong>: Up to 262,144 tokens (latest variants)<\/li>\n<li><strong>Multilingual support<\/strong>: 119 languages and dialects<\/li>\n<li><strong>Vocabulary<\/strong>: 151,936 tokens using BPE tokenization<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Unique Features<\/strong><\/h3>\n<p>Qwen3 incorporates a <strong>hybrid reasoning system<\/strong> supporting both \u201cthinking\u201d and \u201cnon-thinking\u201d modes, allowing users to control computational overhead based on task complexity.<\/p>\n<h2 class=\"wp-block-heading\"><strong>GPT-OSS 20B Technical Specifications<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Architecture Details<\/strong><\/h3>\n<p>GPT-OSS 20B features a <strong>24-layer transformer<\/strong> with <strong>32 MoE experts per layer<\/strong>\u2078. The model activates <strong>4 experts per token<\/strong>, emphasizing wider expert capacity over fine-grained specialization.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Attention Mechanism<\/strong><\/h3>\n<p>The model implements <strong>Grouped Multi-Query Attention<\/strong> with <strong>64 query heads and 8 key-value heads arranged in groups of 8<\/strong>\u00b9\u2070. This configuration supports efficient inference while maintaining attention quality across the wider architecture.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Context and Optimization<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Native context length<\/strong>: 128,000 tokens<\/li>\n<li><strong>Quantization<\/strong>: Native MXFP4 (4.25-bit precision) for MoE weights<\/li>\n<li><strong>Memory efficiency<\/strong>: Runs on 16GB memory with quantization<\/li>\n<li><strong>Tokenizer<\/strong>: o200k_harmony (superset of GPT-4o tokenizer)<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Performance Characteristics<\/strong><\/h3>\n<p>GPT-OSS 20B uses <strong>alternating dense and locally banded sparse attention patterns<\/strong> similar to GPT-3, with <strong>Rotary Positional Embedding (RoPE)<\/strong> for positional encoding\u00b9\u2075.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Architectural Philosophy Comparison<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Depth vs. Width Strategy<\/strong><\/h3>\n<p><strong>Qwen3 30B-A3B<\/strong> emphasizes <strong>depth and expert diversity<\/strong>:<\/p>\n<ul class=\"wp-block-list\">\n<li>48 layers enable multi-stage reasoning and hierarchical abstraction<\/li>\n<li>128 experts per layer provide fine-grained specialization<\/li>\n<li>Suitable for complex reasoning tasks requiring deep processing<\/li>\n<\/ul>\n<p><strong>GPT-OSS 20B<\/strong> prioritizes <strong>width and computational density<\/strong>:<\/p>\n<ul class=\"wp-block-list\">\n<li>24 layers with larger experts maximize per-layer representational capacity<\/li>\n<li>Fewer but more powerful experts (32 vs 128) increase individual expert capability<\/li>\n<li>Optimized for efficient single-pass inference<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>MoE Routing Strategies<\/strong><\/h3>\n<p><strong>Qwen3<\/strong>: Routes tokens through <strong>8 of 128 experts<\/strong>, encouraging diverse, context-sensitive processing paths and modular decision-making.<\/p>\n<p><strong>GPT-OSS<\/strong>: Routes tokens through <strong>4 of 32 experts<\/strong>, maximizing per-expert computational power and delivering concentrated processing per inference step.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Memory and Deployment Considerations<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Qwen3 30B-A3B<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Memory requirements<\/strong>: Variable based on precision and context length<\/li>\n<li><strong>Deployment<\/strong>: Optimized for cloud and edge deployment with flexible context extension<\/li>\n<li><strong>Quantization<\/strong>: Supports various quantization schemes post-training<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>GPT-OSS 20B<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Memory requirements<\/strong>: 16GB with native MXFP4 quantization, ~48GB in bfloat16<\/li>\n<li><strong>Deployment<\/strong>: Designed for consumer hardware compatibility<\/li>\n<li><strong>Quantization<\/strong>: Native MXFP4 training enables efficient inference without quality degradation<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\"><strong>Performance Characteristics<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Qwen3 30B-A3B<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>Excels in <strong>mathematical reasoning, coding, and complex logical tasks<\/strong><\/li>\n<li>Strong performance in <strong>multilingual scenarios<\/strong> across 119 languages<\/li>\n<li><strong>Thinking mode<\/strong> provides enhanced reasoning capabilities for complex problems<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>GPT-OSS 20B<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>Achieves <strong>performance comparable to OpenAI o3-mini<\/strong> on standard benchmarks<\/li>\n<li>Optimized for <strong>tool use, web browsing, and function calling<\/strong><\/li>\n<li>Strong <strong>chain-of-thought reasoning<\/strong> with adjustable reasoning effort levels<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\"><strong>Use Case Recommendations<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Choose Qwen3 30B-A3B for:<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>Complex reasoning tasks requiring multi-stage processing<\/li>\n<li>Multilingual applications across diverse languages<\/li>\n<li>Scenarios requiring flexible context length extension<\/li>\n<li>Applications where thinking\/reasoning transparency is valued<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Choose GPT-OSS 20B for:<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>Resource-constrained deployments requiring efficiency<\/li>\n<li>Tool-calling and agentic applications<\/li>\n<li>Rapid inference with consistent performance<\/li>\n<li>Edge deployment scenarios with limited memory<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n<p>Qwen3 30B-A3B and GPT-OSS 20B represent complementary approaches to MoE architecture design. Qwen3 emphasizes depth, expert diversity, and multilingual capability, making it suitable for complex reasoning applications. GPT-OSS 20B prioritizes efficiency, tool integration, and deployment flexibility, positioning it for practical production environments with resource constraints.<\/p>\n<p>Both models demonstrate the evolution of MoE architectures beyond simple parameter scaling, incorporating sophisticated design choices that align architectural decisions with intended use cases and deployment scenarios.<\/p>\n<p><em>Note: This article is inspired from the <a href=\"https:\/\/www.reddit.com\/r\/LocalLLaMA\/comments\/1mj00g7\/qwen3_vs_gptoss_architecture_width_matters\/?share_id=cA12AqDrF2VRPCJxlcZeF&amp;utm_medium=ios_app&amp;utm_name=iossmf&amp;utm_source=share&amp;utm_term=10\" target=\"_blank\" rel=\"noreferrer noopener\">reddit post<\/a> and diagram shared by Sebastian Raschka. <\/em><\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n<h3 class=\"wp-block-heading\"><strong>Sources<\/strong><\/h3>\n<ol class=\"wp-block-list\">\n<li><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-30B-A3B-Base\">Qwen3 30B-A3B Model Card \u2013 Hugging Face<\/a><\/li>\n<li><a href=\"https:\/\/qwenlm.github.io\/blog\/qwen3\/\">Qwen3 Technical Blog<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-30B-A3B-Base\">Qwen3 30B-A3B Base Specifications<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-30B-A3B-Instruct-2507\">Qwen3 30B-A3B Instruct 2507<\/a><\/li>\n<li><a href=\"https:\/\/qwenlm.github.io\/blog\/qwen3\/\">Qwen3 Official Documentation<\/a><\/li>\n<li><a href=\"https:\/\/qwen.readthedocs.io\/en\/latest\/getting_started\/concepts.html\">Qwen Tokenizer Documentation<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3-8B\">Qwen3 Model Features<\/a><\/li>\n<li><a href=\"https:\/\/openai.com\/index\/introducing-gpt-oss\/\">OpenAI GPT-OSS Introduction<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/openai\/gpt-oss\">GPT-OSS GitHub Repository<\/a><\/li>\n<li><a href=\"https:\/\/console.groq.com\/docs\/model\/openai\/gpt-oss-20b\">GPT-OSS 20B \u2013 Groq Documentation<\/a><\/li>\n<li><a href=\"https:\/\/openai.com\/index\/introducing-gpt-oss\/\">OpenAI GPT-OSS Technical Details<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/blog\/welcome-openai-gpt-oss\">Hugging Face GPT-OSS Blog<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/openai\/gpt-oss-20b\">OpenAI GPT-OSS 20B Model Card<\/a><\/li>\n<li><a href=\"https:\/\/openai.com\/index\/introducing-gpt-oss\/\">OpenAI GPT-OSS Introduction<\/a><\/li>\n<li><a href=\"https:\/\/developer.nvidia.com\/blog\/delivering-1-5-m-tps-inference-on-nvidia-gb200-nvl72-nvidia-accelerates-openai-gpt-oss-models-from-cloud-to-edge\/\">NVIDIA GPT-OSS Technical Blog<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/blog\/welcome-openai-gpt-oss\">Hugging Face GPT-OSS Blog<\/a><\/li>\n<li><a href=\"https:\/\/bestcodes.dev\/blog\/qwen-3-what-you-need-to-know\">Qwen3 Performance Analysis<\/a><\/li>\n<li><a href=\"https:\/\/openai.com\/index\/gpt-oss-model-card\/\">OpenAI GPT-OSS Model Card<\/a><\/li>\n<li><a href=\"https:\/\/huggingface.co\/openai\/gpt-oss-20b\">GPT-OSS 20B Capabilities<\/a><\/li>\n<\/ol>\n<p>The post <a href=\"https:\/\/www.marktechpost.com\/2025\/08\/06\/moe-architecture-comparison-qwen3-30b-a3b-vs-gpt-oss-20b\/\">MoE Architecture Comparison: Qwen3 30B-A3B vs. GPT-OSS 20B<\/a> appeared first on <a href=\"https:\/\/www.marktechpost.com\/\">MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>This article provides a technical comparison between two recently released Mixture-of-Experts (MoE) transformer models: Alibaba\u2019s Qwen3 30B-A3B (released April 2025) and OpenAI\u2019s GPT-OSS 20B (released August 2025). Both models represent distinct approaches to MoE architecture design, balancing computational efficiency with performance across different deployment scenarios. Model Overview Feature Qwen3 30B-A3B GPT-OSS 20B Total Parameters 30.5B 21B Active Parameters 3.3B 3.6B Number of Layers 48 24 MoE Experts 128 (8 active) 32 (4 active) Attention Architecture Grouped Query Attention Grouped Multi-Query Attention Query\/Key-Value Heads 32Q \/ 4KV 64Q \/ 8KV Context Window 32,768 (ext. 262,144) 128,000 Vocabulary Size 151,936 o200k_harmony (~200k) Quantization Standard precision Native MXFP4 Release Date April 2025 August 2025 Sources: Qwen3 Official Documentation, OpenAI GPT-OSS Documentation Qwen3 30B-A3B Technical Specifications Architecture Details Qwen3 30B-A3B employs a deep transformer architecture with 48 layers, each containing a Mixture-of-Experts configuration with 128 experts per layer. The model activates 8 experts per token during inference, achieving a balance between specialization and computational efficiency. Attention Mechanism The model utilizes Grouped Query Attention (GQA) with 32 query heads and 4 key-value heads\u00b3. This design optimizes memory usage while maintaining attention quality, particularly beneficial for long-context processing. Context and Multilingual Support Native context length: 32,768 tokens Extended context: Up to 262,144 tokens (latest variants) Multilingual support: 119 languages and dialects Vocabulary: 151,936 tokens using BPE tokenization Unique Features Qwen3 incorporates a hybrid reasoning system supporting both \u201cthinking\u201d and \u201cnon-thinking\u201d modes, allowing users to control computational overhead based on task complexity. GPT-OSS 20B Technical Specifications Architecture Details GPT-OSS 20B features a 24-layer transformer with 32 MoE experts per layer\u2078. The model activates 4 experts per token, emphasizing wider expert capacity over fine-grained specialization. Attention Mechanism The model implements Grouped Multi-Query Attention with 64 query heads and 8 key-value heads arranged in groups of 8\u00b9\u2070. This configuration supports efficient inference while maintaining attention quality across the wider architecture. Context and Optimization Native context length: 128,000 tokens Quantization: Native MXFP4 (4.25-bit precision) for MoE weights Memory efficiency: Runs on 16GB memory with quantization Tokenizer: o200k_harmony (superset of GPT-4o tokenizer) Performance Characteristics GPT-OSS 20B uses alternating dense and locally banded sparse attention patterns similar to GPT-3, with Rotary Positional Embedding (RoPE) for positional encoding\u00b9\u2075. Architectural Philosophy Comparison Depth vs. Width Strategy Qwen3 30B-A3B emphasizes depth and expert diversity: 48 layers enable multi-stage reasoning and hierarchical abstraction 128 experts per layer provide fine-grained specialization Suitable for complex reasoning tasks requiring deep processing GPT-OSS 20B prioritizes width and computational density: 24 layers with larger experts maximize per-layer representational capacity Fewer but more powerful experts (32 vs 128) increase individual expert capability Optimized for efficient single-pass inference MoE Routing Strategies Qwen3: Routes tokens through 8 of 128 experts, encouraging diverse, context-sensitive processing paths and modular decision-making. GPT-OSS: Routes tokens through 4 of 32 experts, maximizing per-expert computational power and delivering concentrated processing per inference step. Memory and Deployment Considerations Qwen3 30B-A3B Memory requirements: Variable based on precision and context length Deployment: Optimized for cloud and edge deployment with flexible context extension Quantization: Supports various quantization schemes post-training GPT-OSS 20B Memory requirements: 16GB with native MXFP4 quantization, ~48GB in bfloat16 Deployment: Designed for consumer hardware compatibility Quantization: Native MXFP4 training enables efficient inference without quality degradation Performance Characteristics Qwen3 30B-A3B Excels in mathematical reasoning, coding, and complex logical tasks Strong performance in multilingual scenarios across 119 languages Thinking mode provides enhanced reasoning capabilities for complex problems GPT-OSS 20B Achieves performance comparable to OpenAI o3-mini on standard benchmarks Optimized for tool use, web browsing, and function calling Strong chain-of-thought reasoning with adjustable reasoning effort levels Use Case Recommendations Choose Qwen3 30B-A3B for: Complex reasoning tasks requiring multi-stage processing Multilingual applications across diverse languages Scenarios requiring flexible context length extension Applications where thinking\/reasoning transparency is valued Choose GPT-OSS 20B for: Resource-constrained deployments requiring efficiency Tool-calling and agentic applications Rapid inference with consistent performance Edge deployment scenarios with limited memory Conclusion Qwen3 30B-A3B and GPT-OSS 20B represent complementary approaches to MoE architecture design. Qwen3 emphasizes depth, expert diversity, and multilingual capability, making it suitable for complex reasoning applications. GPT-OSS 20B prioritizes efficiency, tool integration, and deployment flexibility, positioning it for practical production environments with resource constraints. Both models demonstrate the evolution of MoE architectures beyond simple parameter scaling, incorporating sophisticated design choices that align architectural decisions with intended use cases and deployment scenarios. Note: This article is inspired from the reddit post and diagram shared by Sebastian Raschka. Sources Qwen3 30B-A3B Model Card \u2013 Hugging Face Qwen3 Technical Blog Qwen3 30B-A3B Base Specifications Qwen3 30B-A3B Instruct 2507 Qwen3 Official Documentation Qwen Tokenizer Documentation Qwen3 Model Features OpenAI GPT-OSS Introduction GPT-OSS GitHub Repository GPT-OSS 20B \u2013 Groq Documentation OpenAI GPT-OSS Technical Details Hugging Face GPT-OSS Blog OpenAI GPT-OSS 20B Model Card OpenAI GPT-OSS Introduction NVIDIA GPT-OSS Technical Blog Hugging Face GPT-OSS Blog Qwen3 Performance Analysis OpenAI GPT-OSS Model Card GPT-OSS 20B Capabilities The post MoE Architecture Comparison: Qwen3 30B-A3B vs. GPT-OSS 20B appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-30023","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - 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Model Overview Feature Qwen3 30B-A3B GPT-OSS 20B Total Parameters 30.5B&hellip;","_links":{"self":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/posts\/30023","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/comments?post=30023"}],"version-history":[{"count":0,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/posts\/30023\/revisions"}],"wp:attachment":[{"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/media?parent=30023"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/categories?post=30023"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/youzum.net\/de\/wp-json\/wp\/v2\/tags?post=30023"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}