41 related articles

Open weight ≠ runnable locally. This article breaks down the hardware barriers, VRAM limits, electricity costs, and parallelism constraints of models like GLM 5.2 and DeepSeek — revealing where open-weight models truly add value: driving cloud competition, not home replication.

A deep dive into LLM inference cost structure and profitability models—from GPU throughput, MoE architecture, and KV Cache to scale effects—revealing the business logic behind API price wars.

Deep dive into Kimi K3: the largest open-weight model at 3 trillion parameters, surpassing Opus-level models in Agentic coding with 896-expert MoE architecture, 1M token context, at Sonnet pricing.

Thinking Machines releases Inkling, an open-source multimodal LLM with near-trillion MoE parameters, 1M token context, Apache 2.0 license. Deep dive into architecture, benchmarks, and pricing.

Colibri uses MoE hot-cold separation and 4-bit quantization to run 744B-parameter models like GLM 5.2 on consumer hardware. Learn about its three-tier memory architecture and speculative decoding.

In-depth review of Poolside's Laguna S 2.1 open-source coding model: MoE architecture, RL training, DGX Spark local deployment, and real-world agentic coding tests with 8B active parameters.

Moonshot AI releases Kimi K3 open-weight model with 2.8T parameters and 1M token context. Our deep dive covers coding, 3D dev, agent capabilities, and safety concerns.

An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.

An in-depth analysis of the open-weights model debate: publicly releasing model weights enables transparency and innovation but raises safety risks. Explores tiered release, red-teaming, and the industry dynamics behind open AI governance.

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?

Ollama lists Kimi 3 with extra pay-per-use fees, breaking subscription expectations and sparking debate over open-weight models and AI service pricing tiers.

Poolside releases its Laguna open-weight model after 18 months of silence, challenging Moonshot's Kimi K3 with 118B vs 2.8T parameters. Can Silicon Valley close the gap with Chinese AI?

Ollama scales up for trillion-parameter open-source models like Kimi K3 and Qwen 3.8. Hugging Face demands $100M from OpenAI, Alibaba Coder goes mobile, and DeepSeek pauses fundraising.

Anthropic has never open-sourced Claude's model weights. As OpenAI, Meta, and Google embrace open source, is Anthropic's AI safety stance genuine caution or a commercial moat? A deep dive into the debate.

Detailed analysis of Kimi K3 quantization deployment options, comparing q4 vs q8 storage requirements, precision trade-offs, and hardware configurations for local self-hosting.

Notion co-founder Simon Last shares Notion's journey from note-taking tool to AI agent workspace: from first tasting GPT-4 to personal and custom agents.

Claude Opus 5 launches next week; Alibaba Qwen integrates into Apple Intelligence for Chinese users; 27B on-device model compressed to 3.8GB; open-source models narrow gap to closed-source by 3.3%.
Mira Murati's New Company Releases 975…
Former OpenAI CTO Mira Murati's Thinking Machines Lab releases a 975B-parameter open-weight LLM, entering the global AI frontier. Analysis of its technical significance, open-weight strategy, and industry impact.

Step-by-step guide to running local open-source models (Qwen3/Gemma) with Ollama, connecting to Codex via CC Switch for zero-token AI coding. Works on a 6GB VRAM laptop.

Thinking Machines Lab releases Inkling, its first open-weight model. Founded by former OpenAI members, the startup enters the LLM market with an open-weight strategy enabling local deployment and private fine-tuning.