SevenTnewS

mixture of experts

7 published articles

xAI / GrokFeatured3 min read

Grok 4.5

Cursor's Grok 4.5 was built by AI agents, not humans. That's the real story.

Cursor's Grok 4.5 is a Mixture-of-Experts model built using reinforcement learning in environments created by earlier AI agents, not humans. It handles complex, long-duration tasks across software engineering, data science, finance, and law, and it's available now.

2026-07-10

Qwen / AlibabaFeatured4 min read

Portfolio strategy

Alibaba's Qwen is building a model for every AI job, not just one to rule them all

Alibaba Cloud's Qwen team quietly released a 35B MoE agent world model, three new ASR models, and an image-generation RL report, revealing a strategic bet on breadth over spectacle in the AI model race.

2026-07-09

Google DeepMindFeatured4 min read

Google DeepMind

Google DeepMind's Gemma 4 turns 26 billion parameters into a reasoning machine that fits on one GPU

Google DeepMind's Gemma 4 technical report details a family of open-weight models with mixture-of-experts, 1M-token context windows, and multi-modal vision. The release signals a strategic play to bring frontier-level reasoning to developers without the cost of proprietary APIs.

2026-07-09

AI3 min read

Artificial intelligence

Aleph alpha's new megakernel library cuts moe inference latency by 200%

Alpha-MoE fuses multiple operations into a single persistent kernel to achieve up to 200% inference speed gains over Triton-based kernels in vLLM and SGLang, targeting FP8-precision MoE models.

2026-07-09

Labs & Research5 min read

Artificial Intelligence

Ai2's EMO makes modular AI emerge from data, not human rules

Ai2's new MoE model, EMO, uses a novel training method that lets expert modules emerge naturally from data, enabling selective expert use with minimal performance loss. The model matches standard MoE performance on benchmarks while offering vastly improved modularity.

2026-07-07

AI2 min read

Deep Dive

Aleph Alpha builds theoretical inference model for DeepSeek: Deriving performance from hardware primitives

Aleph Alpha created a theoretical inference model for DeepSeek v3 to estimate throughput from hardware parameters, analyzing trade-offs across GPU setups to help practitioners optimize performance and cost for large MoE models.

2026-07-05

DeepSeek2 min read

LLM Inference Optimization

Aleph Alpha builds a theoretical inference model to decode DeepSeek V3 performance from hardware primitives

Aleph Alpha's theoretical model predicts DeepSeek V3 inference performance from hardware parameters alone, revealing how GPU count and interconnect bandwidth shift the bottleneck between compute, memory, and communication.

2026-07-04