MoE
31 articles tagged with MoE
NVIDIA releases Nemotron-Labs-3-Puzzle-75B, compressed from 120B to 75B parameters with 2× throughput
NVIDIA has released Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super that reduces the model from 120.7B total/12.8B active parameters to 75.3B total/9.3B active parameters. According to NVIDIA, the model achieves approximately 2× higher server throughput on a single 8×B200 node and increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests while maintaining strong accuracy across benchmarks.
NVIDIA Releases Audex-30B-A3B: Unified Audio-Text Model With 1M Token Context and Speech Generation
NVIDIA released Audex-30B-A3B, a unified audio-text model built on the Nemotron-Cascade-2-30B-A3B backbone. The model handles audio understanding, speech recognition and translation, text-to-speech, audio generation, and speech-to-speech while supporting up to 1M token context length.
DeepSeek Releases V4-Flash: 284B Parameter MoE Model with 1M Context Window at Q8 162GB
Unsloth has released optimized GGUF quantizations of DeepSeek-V4-Flash, a 284B parameter Mixture-of-Experts model that activates 13B parameters and supports 1 million token context windows. The Q8 quantization (UD-Q8_K_XL) runs at 162GB with claimed lossless precision, only 7GB larger than the Q4 variant.
Tencent Releases Hy3: 295B-Parameter MoE Model with 21B Active Parameters at 256K Context
Tencent has released Hy3, a 295-billion parameter Mixture-of-Experts model with 21 billion active parameters and 3.8 billion MTP layer parameters. The model features a 256K context window and is released under Apache 2.0 license, with pricing not yet disclosed.
NVIDIA releases Nemotron-Labs-TwoTower-30B: block-wise diffusion model claims 2.42× faster generation at 98.7% baseline
NVIDIA released Nemotron-Labs-TwoTower-30B-A3B-Base-BF16, a block-wise diffusion language model that generates text by denoising blocks of tokens in parallel rather than sequentially. According to NVIDIA, the model achieves 2.42× the wall-clock generation throughput of its autoregressive baseline while retaining 98.7% of aggregate benchmark quality.
Alibaba Qwen Releases 35B Language World Model for Agent Environment Simulation Across 7 Domains
Alibaba's Qwen team released Qwen-AgentWorld-35B-A3B, a 35 billion parameter language world model designed for agentic environment simulation. The model covers seven domains—MCP tool calling, Search, Terminal, Software Engineering, Android, Web, and OS—in a single model with a 262,144 token context window.
NVIDIA Releases Quantized DiffusionGemma 26B: 1,100+ Tokens/Second with 256K Context Window
NVIDIA released a quantized version of Google DeepMind's DiffusionGemma 26B A4B IT, a multimodal model with 25.2B total parameters (3.8B active) that processes text, image, and video inputs. The NVFP4-quantized model achieves generation speeds exceeding 1,100 tokens per second on NVIDIA H100 GPUs while supporting a 256K token context window.
Moonshot AI releases Kimi K2.7 Code with 1T parameters, 256K context window, 30% lower thinking token usage
Moonshot AI has released Kimi K2.7 Code, a 1 trillion parameter Mixture-of-Experts model designed for long-horizon coding tasks. The model features a 256K context window and reduces thinking token usage by approximately 30% compared to its predecessor K2.6.
Cohere Releases North Mini Code 1.0: 30B-Parameter MoE Model With 256K Context for Agentic Coding
Cohere Labs has released North Mini Code 1.0, a 30B-parameter sparse Mixture-of-Experts model with 3B active parameters and a 256K context window. The Apache 2.0-licensed model is optimized for agentic software engineering, featuring 128 experts with 8 activated per token, and trained specifically for tool use in coding tasks.
Nvidia releases Nemotron 3 Ultra: 550B-parameter MoE model with 1M context window for agentic workflows
Nvidia has released Nemotron 3 Ultra, a 550-billion parameter mixture-of-experts model with 55 billion active parameters and support for up to 1 million token context windows. The model uses a hybrid Transformer-Mamba architecture and is designed specifically for long-running agentic workflows including agent orchestration, coding agents, and complex enterprise tasks.
NVIDIA Nemotron 3 Ultra launches on AWS SageMaker with 550B parameters, 1M token context window
NVIDIA Nemotron 3 Ultra is now available on Amazon SageMaker JumpStart with 550 billion total parameters and 55 billion active parameters. The model features a hybrid Transformer-Mamba Mixture-of-Experts architecture and supports context windows up to 1 million tokens, targeting agentic AI workloads.
JetBrains Releases Mellum2: 12B MoE Model With 2.5B Active Parameters for Code and Text
JetBrains has released Mellum2, a 12-billion parameter Mixture-of-Experts model that activates only 2.5 billion parameters per token. The open-source model is designed for code generation, RAG pipelines, and agent workflows with 2x faster inference than similar-sized models.
StepFun Releases Step-3.7-Flash: 198B-Parameter Sparse MoE Model With 256K Context in GGUF Format
StepFun has released Step-3.7-Flash, a 198B-parameter sparse Mixture-of-Experts vision-language model that activates approximately 11B parameters per token. The model supports a 256K context window, native image understanding via a 1.8B-parameter vision encoder, and offers three selectable reasoning levels.
StepFun launches Step 3.7 Flash: 196B MoE model with 256K context and adjustable reasoning levels at $0.20/$1.15 per 1M
StepFun has released Step 3.7 Flash, a 196B-parameter Mixture-of-Experts model that activates approximately 11B parameters per token. The multimodal model supports a 256K context window and introduces selectable reasoning levels (high/medium/low), priced at $0.20 per 1M input tokens and $1.15 per 1M output tokens.
Tencent Releases Hy-MT2 Translation Models: 1.8B, 7B, and 30B-A3B Support 33 Languages
Tencent released Hy-MT2, a family of multilingual translation models available in 1.8B, 7B, and 30B-A3B (MoE) sizes. All models support translation among 33 languages and follow translation instructions in multiple languages. The 1.8B model can be compressed to 440MB using 1.25-bit AngelSlim quantization.
DeepSeek Releases V4 Flash: 284B-Parameter MoE Model with 1M Context Window, Free via OpenRouter
DeepSeek has released V4 Flash, a Mixture-of-Experts model with 284B total parameters and 13B activated parameters per forward pass. The model supports a 1M-token context window and is available free through OpenRouter, targeting high-throughput coding and chat applications.
Zyphra Releases ZAYA1-8B: 8.4B Parameter MoE Model with 760M Active Parameters Matches 80B+ Models on Math Benchmarks
Zyphra has released ZAYA1-8B, a mixture-of-experts language model with 760M active parameters and 8.4B total parameters. The model scores 89.1% on AIME 2026, competitive with models exceeding 100B parameters, while maintaining efficiency for on-device deployment.
NVIDIA releases Nemotron-3-Nano-Omni-30B, a 31B-parameter multimodal model with 256K context and reasoning mode
NVIDIA released Nemotron-3-Nano-Omni-30B-A3B, a multimodal large language model with 31 billion parameters that processes video, audio, images, and text with up to 256K token context. The model uses a Mamba2-Transformer hybrid Mixture of Experts architecture and supports chain-of-thought reasoning mode.
NVIDIA Releases Nemotron 3 Nano Omni: 31B Multimodal Model With 256K Context and Reasoning Mode
NVIDIA released Nemotron 3 Nano Omni, a 31B parameter (30B active, 3B per token) multimodal model supporting video, audio, image, and text inputs. The model features a 256K token context window, reasoning mode with chain-of-thought, and tool calling capabilities.
NVIDIA Releases Nemotron 3 Nano Omni: 31B-Parameter Multimodal Model with 256K Context and Reasoning Mode
NVIDIA has released Nemotron 3 Nano Omni 30B-A3B, a multimodal large language model with 31 billion parameters using a Mamba2-Transformer hybrid Mixture of Experts architecture. The model supports video, audio, image, and text inputs with a 256K token context window and includes a dedicated reasoning mode with chain-of-thought capabilities.
NVIDIA Nemotron 3 Nano Omni: 30B-parameter multimodal model launches on AWS SageMaker with 131K token context
NVIDIA has launched Nemotron 3 Nano Omni on Amazon SageMaker JumpStart, a multimodal model with 30 billion total parameters (3 billion active) that processes video, audio, images, and text in a single inference pass. The model features a 131K token context window and uses a Mamba2 Transformer Hybrid MoE architecture combining three specialized encoders.
NVIDIA Releases Nemotron 3 Nano Omni: 30B-A3B Multimodal Model With 100+ Page Document Support
NVIDIA released Nemotron 3 Nano Omni, a 30B-A3B Mixture-of-Experts model that processes text, images, video, and audio. The model uses a hybrid Mamba-Transformer architecture with 128 experts and achieves 65.8 on OCRBenchV2-En and 72.2 on Video-MME, while delivering up to 9x higher throughput on multimodal tasks compared to alternatives.
Xiaomi Releases MiMo-V2.5-Pro: 1.02T Parameter MoE Model with 1M Context Window
Xiaomi has released MiMo-V2.5-Pro, an open-source Mixture-of-Experts model with 1.02 trillion total parameters and 42 billion active parameters. The model supports up to 1 million tokens context length and claims 99.6% on GSM8K and 86.2% on MATH benchmarks.
Alibaba Releases Qwen3.6 Max Preview: 1 Trillion Parameter MoE Model With 262K Context Window
Alibaba Cloud has released Qwen3.6 Max Preview, a proprietary frontier model built on sparse mixture-of-experts architecture with approximately 1 trillion total parameters. The model supports a 262,144-token context window and features integrated thinking mode for multi-turn reasoning, priced at $1.30 per million input tokens and $7.80 per million output tokens.
DeepSeek Releases V4 Pro: 1.6T Parameter MoE Model with 1M Token Context at $1.74/M Input Tokens
DeepSeek has released V4 Pro, a Mixture-of-Experts model with 1.6 trillion total parameters and 49 billion activated parameters. The model supports a 1-million-token context window and costs $1.74 per million input tokens and $3.48 per million output tokens.
DeepSeek V4 Flash Released: 284B Parameter MoE Model with 1M Context Window at $0.14 per Million Tokens
DeepSeek has released V4 Flash, a Mixture-of-Experts model with 284B total parameters and 13B activated parameters per request. The model supports a 1,048,576-token context window and is priced at $0.14 per million input tokens and $0.28 per million output tokens.
Tencent Releases Hy3-Preview: 295B-Parameter MoE Model with 21B Active Parameters
Tencent has released Hy3-preview, a 295-billion-parameter Mixture-of-Experts model with 21 billion active parameters and a 256K context window. The model scores 76.28% on MATH and 34.86% on LiveCodeBench-v6, with particularly strong performance on coding agent tasks.
Tencent Releases Hy3 Preview MoE Model with 262K Context and Three Reasoning Modes
Tencent has released Hy3 Preview, a Mixture-of-Experts model offering 262,144 token context window and three configurable reasoning modes (disabled, low, high) for production agentic workflows. The model is available for free through OpenRouter.
Arcee AI Releases Trinity Large Preview: 400B-Parameter MoE Model with 512K Context Window
Arcee AI has released Trinity Large Preview, a 400B-parameter sparse Mixture-of-Experts model with 13B active parameters per token using 4-of-256 expert routing. The model supports context windows up to 512K tokens and is available with open weights under permissive licensing.
Moonshot AI Releases Kimi K2.6: 1T-Parameter MoE Model with 256K Context and Agent Swarm Capabilities
Moonshot AI has released Kimi K2.6, an open-source multimodal model with 1 trillion total parameters (32B activated) and 256K context window. The model achieves 80.2% on SWE-Bench Verified, 58.6% on SWE-Bench Pro, and supports horizontal scaling to 300 sub-agents executing 4,000 coordinated steps.
Alibaba Releases Qwen3.6-35B-A3B: 35B Parameter MoE Model with 262K Context Window
Alibaba has released Qwen3.6-35B-A3B, the first open-weight model in the Qwen3.6 series. The model features 35B total parameters with 3B activated, a native 262K context window extensible to 1.01M tokens, and achieves 73.4% on SWE-bench Verified using 256 experts with 8 activated per token.