Tools, frameworks, and models for local AI
18 resources found for "Generazione"
Il chip Apple Silicon di punta per l'IA locale: 18 core CPU e 40 core GPU con memoria unificata fino a 128GB a 614 GB/s. Il Neural Accelerator su ogni core GPU raddoppia la velocità dei modelli MoE in decode rispetto alla generazione M4: ~88 tok/s su gpt-oss-120b, il più veloce della classe.
Black Forest Labs (Munich, Germany): the FLUX models, today's reference point for high-quality open image generation. The FLUX models in the catalog are the child resources.
Cloudflare's 27B multimodal decision model (base Qwen3.8-27B): reads a state (text, JSON, images, video) and a schema of typed questions and returns a probability per option in a single pass, with no text generation. Jev and SystemOne API compatible.
Very few steps, high quality, and strong text rendering. The distilled FLUX variant, built for speed.
One-click SDXL image generation, Midjourney-style: zero parameters to manage. The simplest way to produce quality images.
Kandinsky Lab's foundation models for synchronized video and audio generation: from a prompt they generate video clips with aligned audio, released as open weights. Top paper of the day on Hugging Face (2026-10-06), with checkpoints and Spaces already available.
Fast, open video generation: text or image to 768px video with good motion quality. One of the fastest open video models.
326M-parameter autoregressive diffusion model for interactive real-time humanoid motion: online text prompts, kinematic constraints (root trajectories, keyframes, sparse poses) and sequences up to 8 seconds. Trained on Bones Rigplay 1 (27-joint Core skeleton, 20 fps), optimized for NVIDIA Ampere, Hopper and Blackwell GPUs. NVIDIA Open Model Agreement license, commercial use allowed. On Hugging Face since October 7, 2026.
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.
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 16GB d'ingresso della generazione Blackwell: GB206, 16GB GDDR7 a 448 GB/s, 180W, PCIe 5.0 x8. Lancio a 429 USD: il modo più economico di far girare Qwen3.8-27B e i modelli 30B+ in Q4 su una singola scheda. Prezzo street 2026 più alto per la carenza di GDDR7.
Alibaba's 20B (MMDiT) image-generation model: excellent in-image text comprehension, precise editing, and an Apache 2.0 license. The base of the Qwen-Image family, of which 2.1 is the compact 7B variant.
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 multimodale della generazione Qwen3.8 di Alibaba: 27,78B di parametri, input testo+immagini+video e contesto nativo di 262K token. Licenza Apache 2.0. L'attenzione ibrida (DeltaNet lineare + GQA) riduce la KV cache di circa il 75% rispetto a un transformer standard: in Q4_K_M sta su una singola GPU da 24GB.
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.
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.
The image-generation classic: fast, light, and with the largest LoRA/ControlNet ecosystem of all. Perfect for 6-8GB GPUs.
Turns an image into a short ~2s video clip. The entry point for local video generation; 16GB VRAM recommended.
