Tools, frameworks, and models for local AI
24 resources found for "Qwen"
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.
La Radeon con la maggiore capacità consumer: 24GB GDDR6 a 960 GB/s (bus 384-bit). Per l'IA locale la capacità batte la novità: mentre le schede da 16GB non caricano i modelli 27B, qui Qwen3.8-27B e i 32B in Q4 ci stanno con contesto ampio. 960 GB/s è il 96% della RTX 4090. ROCm ufficiale (gfx1100), 355W.
La GPU RDNA 4 di AMD: 16GB GDDR6 su bus 256-bit (640 GB/s), 64 CU, 304W. Con ROCm 7 la prima Radeon consumer con supporto ufficiale su Linux (gfx1201, senza workaround): ~92 tok/s su gpt-oss-20b, ~52 tok/s su Qwen 14B. Il 640 GB/s è il 43% in più della RTX 5060 Ti 16GB: la carta 16GB più veloce per modelli 9-20B, ma il 16GB non tiene un 27B dense.
An open decision model from Autotrust, 35B parameters (3B active), a fine-tune of Qwen3.5-35B-A3B for text classification and risk scoring. In NVFP4 quantization it occupies about 25.6 GB of VRAM: it runs on a 32 GB GPU with no frontier inference needed.
27B reasoning model with ternary weights at 1.72 bits per weight, derived from Qwen3.8-27B with hybrid attention (~75% linear). In GGUF PTQ1_0 it takes 5.95 GB, retains 98.2% of the FP16 intelligence and reaches ~47 tok/s on an Apple M5 Max laptop, with a 262K-token context. Apache 2.0 license. Requires Prism ML's llama.cpp fork (ternary kernels are not in stock llama.cpp).
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.
A 5.6B open-weights image model for text-to-image, single-reference editing and multi-reference composition in one model, with placement boxes in the prompt. Qwen3-VL-4B text encoder. In BF16 (11.1GB), FP8 and NVFP4 (5.3GB). FLUX Kommunity license (non-commercial + qualified-user exception).
Hugging Face's Python library for running the pipelines of generative models: image, video, and audio. The standard way to run models like Qwen-Image locally, with CUDA support and CPU offload for lower-memory GPUs.
Open source framework for unified fine-tuning of 100+ LLMs and VLMs (Qwen, Llama, Mistral, Gemma): from full SFT to LoRA, QLoRA and DPO, with a built-in WebUI and startup from a single YAML file. The community's reference project (ACL 2024).
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.
Blackwell GB203 da fascia alta: 16GB GDDR7 su bus 256-bit (896 GB/s), 8.960 CUDA core, 300W. Il 16GB rende possibile Qwen3.8-27B o i modelli 30B+ in Q4 con contesto ampio. Lancio a 749 USD, febbraio 2025.
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.
Ultra-compact 0.5B-parameter model. Runs on any hardware, even embedded: ideal for lightweight summarization and low-power local devices.
Clear step up in reasoning and instruction quality over 8B models. Excellent for coding, agents and structured writing on 16GB GPUs or Macs with 32GB RAM.
Near-70B quality at a fraction of the size: the sweet spot for advanced use. Excellent in Italian, code and reasoning. Needs 24GB+ VRAM.
3B-parameter model with excellent multilingual support, including Italian. Good balance of quality and hardware requirements for summarization and drafting.
The top open dense model of its class, with first-class multilingual support for Italian, French and other European languages. Needs 48-80GB setups.
Mixture-of-Experts model: 30B total params but only 3.3B active per token. Small-model speed with mid-size quality; the best speed/quality ratio in its class.
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.
A 35B-parameter MoE model with ~3B active per token from the Qwen family, Apache-2.0. It is the fine-tune base of JEV-35B. Suited to 40GB+ GPUs in BF16 or to 4/8-bit quantizations for smaller hardware.
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.
100% local macOS dictation app: on-device transcription with Whisper and a rewrite pass with a local LLM (Qwen) on the M-series GPU. Text lands in the target app; no data touches the cloud.
