397 related articles

Alibaba's Qwen3.8 challenges larger models with a 2.4T-parameter MoE architecture, claiming second only to Gemini. A deep dive into MoE mechanics, continuous updates, two-speed release strategy, and real local deployment requirements.

Agent A1 is a 35B MoE open-source model from Shanghai's Intern Science, activating only 3B params. Hits 95 tok/s on M4 Mac Studio. Supports 256K context, tool calling, and free local deployment via Ollama.

MiniMax M3 is a 428B MoE model. Its 23B active parameters reflect per-token compute, not VRAM needs. Learn the MoE trade-offs, quantization options, and deployment paths to avoid the most common misconception.

An indie dev attempts to train a CPU-native LLM on $0 budget using ternary quantization, sparsity, and fine-grained MoE — with pre-registered success criteria and full public reporting.

A 15-year-old trained Tiny-MoE, a 200M-parameter MoE language model from scratch using free Kaggle GPUs, featuring MLA attention, RoPE+YaRN, and native PyTorch.

Tencent Hunyuan Hy3 launches with a 295B MoE architecture activating just 21B params and 256K context. Hallucination cut from 12.5% to 5.4%, MRCR nearly doubled, with MTP and EAGLE decoding and Day-0 SGLang support.

Tencent open-sources Hunyuan V3: 295B MoE model with 21B active params, claims to beat DeepSeek-V4-Pro. Supports FP8, vLLM/SGLang — but 256K context and no multimodal are key drawbacks.
Tech FrontiersDeep dive into StepFun AI's Step 3.7 Flash, a 198B sparse MoE vision-language model with 256K context and 3-level reasoning, excelling in multimodal understanding, AI coding, and Agent tool orchestration.
Tech FrontiersLiquid AI releases LFM2.5-8B-A1B, a MoE model with 8B total params but only 1.5B active, matching 6B-class models in tool calling. Supports 128K context, local deployment, multilingual, with SGLang Day-0 support.
Tech FrontiersDeep dive into Moonshot AI's fully open-sourced Kimi K2.5: 1T parameter MoE architecture, Vision-to-Code capabilities, and 100-Agent parallel cluster system topping open-source benchmarks.
Tech FrontiersWukong 2.2P 35B MOE model is now open source. Using adversarial hybrid distillation, it outperforms Qwen3.6-27B. Runs at 158 tokens/s on RTX 4090 with only 8.9GB VRAM. Supports 256K context.
Deep DivesA deep dive into Microsoft's open-source Tutel MoE optimization library, supporting FP8, NVFP4, and MXFP4 multi-precision computation for DeepSeek, Kimi-K2, Qwen3, and other leading MoE models.

Poolside launches Laguna open-weight model after 18 months of silence, pitting 118B parameters against Kimi K3's 2.8 trillion. Can Silicon Valley's open-source push close the gap with Chinese AI?

Anthropic releases Claude Opus 5 with near-frontier performance at lower prices. Same day, Jensen Huang co-signs open-weight letter with 20+ companies while DeepSeek fundraising rumors surface.

Moonshot AI launches Kimi K3 reasoning model with performance rivaling Claude and OpenAI's top models at one-third the price. The US-China AI gap narrows from 6-12 months to just 3 months.

DeepSeek's open source model shakes Silicon Valley. OpenAI defends closed source while Microsoft, NVIDIA, and Meta back open ecosystems. Analysis of the AI open/closed source debate, Apple-Micron chip tensions, and AI-driven historical disinformation.

Chinese open-source models DeepSeek and Kimi K3 are challenging OpenAI's closed-source dominance. Analyzing the business logic, chip ecosystems, and US-China strategic dynamics behind the open vs. closed AI debate.

Fields Medal winner Jacob Tsimerman joins OpenAI's safety team on award day, declaring math careers won't survive. Meanwhile, NVIDIA finances a $250B data center and Kimi K3 open-sources 2.8T parameters.

Fields Medal winner Jacob Tsimerman joins OpenAI's safety team on award day, saying math careers won't survive. NVIDIA finances a $250B data center. Kimi K3 opens a 2.8T-parameter model.

Kimi K3 officially launches on Ollama Cloud as an "extra high usage" model. This guide covers free tier quotas, cloud inference experience, technical advantages, and how developers can seamlessly call this high-performance LLM.