Tools, frameworks, and models for local AI
17 resources found for "Locale"
Bespoke Labs' 9B decision model: it runs entirely locally via Ollama 0.35 and answers typed questions (true/false, numeric scale, multiple choice) through the new systemone API. Built for fast classification in automated pipelines, no cloud.
DeepSeek's MoE model with 552B total parameters and 8-16B active per token. 1M-token context, MIT license: free commercial use. Built for online serving, too large for local use on a single PC.
La taglia 12B della famiglia Gemma 4 di Google: 11,95B dense, contesto 256K e multimodalità integrata senza encoder separati (testo+immagini+audio). Licenza Apache 2.0. In Q4_K_M occupa circa 7,4GB: il punto di equilibrio tra qualità e requisiti hardware per l'uso locale su 8-16GB.
Compact model from Google's Gemma 4 family: 8B total parameters, of which 4.5B effective thanks to per-layer embeddings (PLE). 131K-token context, 140+ languages, text+image and audio input. The entry size of the Gemma 4 family for local use on 8-16GB.
82-million-parameter speech synthesis model on a VITS architecture, Apache-2.0 for the weights, with voices in dozens of languages and surprisingly high quality for its size. Runs efficiently on CPU alone.
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.
The de-facto local workhorse: solid quality, 131K-token context and a huge GGUF quantization ecosystem. The standard choice for chat and assistants.
A 500-million-parameter foundation model from NVIDIA for tabular data: prediction, imputation and generation on structured datasets, with OpenMDW 1.1 support and ready-to-use checkpoints. An example of local AI on business data, not text generation.
Modello di sintesi vocale multilingue di NVIDIA: 357M parametri, 12 lingue (italiano e francese inclusi), architettura encoder-decoder transformer. Compatto e pensato per esecuzione in locale a bassa latenza; pesi disponibili su Hugging Face.
NVIDIA's vision encoder-decoder family for local document understanding: markdown, LaTeX formulas, bounding boxes and a semantic class per element, in reading order. ViT-H (C-RADIO) + 10-block mBART decoder, all under 1 billion parameters. Locally via vLLM (v0.20–v0.26 for 2.0) or Transformers, on NVIDIA GPUs (Turing through Blackwell).
11B end-to-end full-duplex voice model: audio understanding and generation in a single architecture, without the cascaded ASR→LLM→TTS stack. First open full-duplex model with tool calling during conversation, ~450ms responses, interruptions, and natural turns.
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.
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.
Il modello dense 8B della famiglia Qwen3: 8,19B di parametri, contesto nativo di 40K token (espandibile con YaRN) e ragionamento in modalità think/non-think attivabile per prompt. Licenza Apache 2.0. È il punto di equilibrio per l'uso locale su GPU da 8-12GB: in Q4_K_M occupa circa 5GB, lasciando spazio per contesto lungo.
0.6B safety-moderation model with a token-level classifier for real-time monitoring during incremental generation. Three severity tiers (safe, controversial, unsafe) and 119 languages: small and local, ideal for filtering outputs without the cloud.
Turns an image into a short ~2s video clip. The entry point for local video generation; 16GB VRAM recommended.
Il modello di trascrizione di riferimento per l'IA locale: 809M di parametri, 99 lingue, licenza MIT. Versione distillata di Large-v3 con decoder ridotto da 32 a 4 layer: trascrive circa 8 volte più veloce con perdita di accuratezza trascurabile. Gira in tempo reale su una GPU consumer e anche solo su CPU.
