Tools, frameworks, and models for local AI
14 resources found for "Open-weight"
Alibaba Group: the Qwen models are among the world's most performant open-weight LLMs, released under permissive licenses. The Qwen models in the catalog are the child resources.
DeepSeek (Shenzhen, China): the most efficient open-weight LLMs in their class, with a focus on low-activation MoE. DeepSeek V4.1-Flash pushes 552B total parameters with ~8B active and a 1M context. The models in the catalog are the child resources.
Google DeepMind: the Gemma models are Google's open-weight line — compact, multilingual and multimodal, under a permissive license. The Gemma 4 family (2026) reaches a 256K context. The models in the catalog are the child resources.
Modello MoE open-weight di inclusionAI: 560B parametri totali, 25B attivi per token, architettura ibrida (attenzione lineare KDA + Gated MLA), finestra di contesto fino a 1M di token, licenza MIT. Reasoning ibrido con modalità di pensiero on/off. A 4 bit entra in 512GB di memoria unificata.
Aleph Alpha's MoE: 78.1B parameters (3.46B active per token), Apache 2.0, FP8 weights, context up to 1M tokens, reasoning mode and tool calling. Built for mission-critical workloads on local hardware.
An online guide matching open-weights models to real hardware: for every GPU, Mac and mini-PC it shows what runs, how much memory it needs and at how many tokens per second, with guides on quantization and memory. The right tool for the own-hardware path.
Meta Platforms: the Llama family is the world's most widely used open-weight LLM line, from 1B to 405B parameters, with a commercial-use license. The Llama models in the catalog are the child resources.
Mistral AI (Paris, France): the most efficient European open-weight LLMs, with permissive licenses and a focus on privacy and localization. The Mistral models in the catalog are the child resources.
Serverless platform for running code and AI models on GPUs: you pay for real seconds of use, it scales to zero when idle, and you can run any open-weights model. One of the routes to inference on rented hardware without managing servers.
OpenAI: beyond its cloud models (GPT-5), the gpt-oss series (2025) made its reasoning models open-weight and free, in MXFP4 — the first large reasoning models runnable on a laptop. Whisper is the open standard for speech transcription. The models in the catalog are the child resources.
Il modello di ragionamento open-weight di OpenAI: MoE con 21B di parametri totali e 3,6B attivi (32 esperti, top-4), contesto 128K e quantizzazione MXFP4 nativa. Licenza Apache 2.0: i pesi partono già quantizzati a 4 bit, senza passo di conversione. Sta in 16GB di VRAM e gira anche su RAM unificata o CPU.
Cloud gateway for LLM inference: a single API endpoint for hundreds of models (including open-weight MoE), with per-token pricing and automatic routing across providers. For Ling 3.1 Flash: $0.075/M input, $0.225/M output.
7B open-weight model (32-layer DiT) that unifies text-to-image generation up to 2048x2048, editing with up to 10 reference images, and native RGBA transparency. Designed to run locally on 16GB+ VRAM; supported by diffusers and ComfyUI.
Stability AI (London, UK): the Stable Diffusion models for image, video and audio generation, open-weight with permissive licenses. The Stable models in the catalog are the child resources.
