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#mixture-of-experts

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AI News

Nemotron 3.5 Lightning: Nvidia's 3B-Active Agent Workhorse

Nvidia released Nemotron 3.5 Lightning on August 11, 2026: an open 30B mixture-of-experts model with 3B active parameters, licensed under OpenMDW-1.1 with weights, training data and recipes included. It targets the execution layer of long-running agents rather than frontier reasoning, reaching 86% accuracy on PinchBench while completing 10,000 tasks 30% faster than Qwen3.6 35B, and up to 4x the output speed of similar-sized models. Speed comes from baked-in multi-token prediction plus DSpark and DFlash draft models, with NVFP4 and BF16 checkpoints. It runs on Jetson, RTX 5090 and DGX Spark via LM Studio, llama.cpp, Ollama and Unsloth, and ships alongside NeMo Switchyard for routing planning to frontier models and execution to Lightning.

Aug 12, 2026 · 6 min read
AI News

Qwen3.8-Max: Alibaba's 2.4T-Parameter Flagship Goes Live

Alibaba released Qwen3.8-Max on August 3, 2026, a 2.4-trillion-parameter mixture-of-experts model with a 1M-token context window, multimodal (text/image/video) input, and $2/$6 per-million-token pricing. Benchmarks are self-reported and lead on multimodal and agentic tasks while trailing the frontier on pure software engineering. Open weights for the flagship and a deployable 27B checkpoint are promised the following week.

Aug 4, 2026 · 4 min read
AI News

DeepSeek V4: 1.6T Open Weights and 1M Context, Now the Default

DeepSeek released V4 as two open-weight mixture-of-experts models: V4-Pro (1.6T total / 49B active) and V4-Flash (284B / 13B active), both with a 1M-token default context and 384K max output. A novel token-wise compression plus DeepSeek Sparse Attention (DSA) makes the long window affordable. API pricing is aggressive (V4-Flash $0.14/M input, $0.28/M output; V4-Pro $0.435/$0.87), and the old deepseek-chat and deepseek-reasoner endpoints were retired after July 24, 2026. Reported ~80.6% on SWE-bench Verified.

Aug 1, 2026 · 5 min read
AI News

Laguna S 2.1: Poolside's 118B Open-Weight Coding Model

Poolside released Laguna S 2.1 on July 21, 2026, a 118B-parameter Mixture-of-Experts coding model activating ~8B params per token, with a 1M-token context and a permissive OpenMDW-1.1 license. First-party benchmarks show 78.5% on SWE-Bench Multilingual, but independent verification is still pending. Day-one FP8/NVFP4/INT4 and GGUF builds make it genuinely self-hostable.

Jul 31, 2026 · 5 min read
Deep Dives

GRPO: The Critic-Free RL Algorithm Behind DeepSeek-R1

GRPO (Group Relative Policy Optimization) is a critic-free reinforcement learning algorithm introduced in the DeepSeekMath paper (arXiv 2402.03300). Instead of training a separate value model like PPO, it samples a group of responses per prompt and computes each response's advantage relative to the group's mean and standard deviation. It powered DeepSeek-R1's emergent reasoning and is the central baseline for reinforcement learning with verifiable rewards in 2026, spawning variants like Dr. GRPO, DAPO, and GSPO.

Jul 22, 2026 · 6 min read
AI News

Inkling: Mira Murati's Thinking Machines Ships Its First Open Model

Thinking Machines Lab, founded by ex-OpenAI CTO Mira Murati, released Inkling on July 15, 2026 — an open-weight mixture-of-experts model with 975B total parameters (41B active), trained on 45 trillion multimodal tokens. The company openly says it isn't the strongest model available; instead it's a customizable foundation enterprises fine-tune via the Tinker platform. The release doubles as an argument that owned, adaptable models beat rented one-size-fits-all APIs.

Jul 18, 2026 · 5 min read
AI News

Kimi K3: Moonshot's 2.8T Open Model Nears the Frontier

Moonshot AI released Kimi K3 on July 16, 2026, a 2.8-trillion-parameter open Mixture-of-Experts model that activates 16 of 896 experts, ships native vision and a 1M-token context, and leads benchmarks like SWE Marathon, BrowseComp, and OmniDocBench while trailing Fable 5 and GPT-5.6 Sol overall. Weights release July 27 under a Modified MIT license.

Jul 17, 2026 · 5 min read
Deep Dives

Mixture of Experts: How Sparse Models Beat Dense LLMs

Mixture of Experts (MoE) replaces a transformer's single feed-forward network with many smaller expert networks plus a learned router that sends each token to only its top-k experts (sparse activation). This decouples total parameters (which set memory) from active parameters (which set compute). Mixtral 8x7B has 46.7B total but 12.9B active via top-2 routing; DeepSeek-V3 has 671B total but 37B active (5.5%) using 256 routed experts plus one shared expert and top-8 routing. The design traces to Shazeer et al. (2017) and Google's Switch Transformer (2021, top-1 routing, 1.6T params). Trade-offs include memory footprint, load-balancing difficulty, training instability, communication overhead, and harder fine-tuning.

Jul 10, 2026 · 6 min read
AI News

MAI-Thinking-1: Microsoft's First In-House Reasoning Model

Microsoft unveiled MAI-Thinking-1 at Build 2026, its first reasoning model trained in-house without distillation. The 35B-active, ~1T-total MoE has a 256k context window, scores 97.0% on AIME 2025 and matches Claude Opus 4.6 on SWE-Bench Pro. It's in private preview on Microsoft Foundry.

Jun 23, 2026 · 5 min read
AI News

Kimi K2.7-Code: A 30% Token Cut With a Benchmark Asterisk

Moonshot AI's Kimi K2.7-Code is an open-weights, OpenAI-compatible coding model (1T-param MoE, 32B active, 256K context) claiming a 30% cut in reasoning tokens and a narrow win over Claude Opus 4.8. But all published benchmarks are Moonshot's own proprietary suites, with no independent results yet, so the efficiency claims remain unverified.

Jun 14, 2026 · 5 min read
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