Sunday, 20 September 2026 | Mise à jour quotidienne L'intelligence artificielle au service des constructeurs

Nom de l'auteur : Mustafa Ihsan

Mustafa Ihsan is the founder and editor of Convly.ai. He built and maintains the site's live AI models database, its price-performance index, and its free calculators for VRAM requirements, API costs and self-hosting economics. He writes about model pricing, benchmark results and the hardware needed to run AI models locally, and consistently prefers measured numbers to vendor claims.

10× — the spread we measured. LM Studio.
Tutoriels

LM Studio : Guide complet pour exécuter des modèles d’IA locaux

TL;DR:LM Studio is a free desktop application for running large language models locally on your computer without coding or command-line workDownload models directly from Hugging Face through the built-in browser, supports GGUF format with automatic quantization selectionIncludes chat UI, local API server (OpenAI-compatible), and automatic hardware acceleration (CUDA, Metal, CPU)Requires sufficient VRAM/RAM based on model size—typically 8GB minimum for 7B models, 24GB+ for 13B modelsLM Studio is a desktop application that lets you download, run, and interact with large language models on your own computer.

2× — the spread we measured. Ollama GPT OSS.
Tutoriels

Ollama GPT OSS : Guide complet pour exécuter les modèles ouverts d’OpenAI

TL;DRgpt-oss:20b runs in ~16GB memory (fits most gaming GPUs), gpt-oss:120b needs ~70GB (single 80GB GPU or split across consumer cards)Install with ollama pull gpt-oss:20b or ollama pull gpt-oss:120b, then run with ollama run gpt-oss:20bBoth variants use MXFP4 quantisation at 4.25 bits per parameter and support 128K context windowsReleased by OpenAI as open-weight models in partnership with Ollama, comparable to Llama 3.1 and Qwen 2.5 in quality OpenAI released gpt-oss as open-weight models in August 2025, distributed exclusively through Ollama.

10% — measured, not claimed. Hugging Face Datasets.
Tutoriels

Hugging Face Datasets : Référence pour les développeurs

A Hugging Face dataset is a structured data collection hosted on the Hugging Face Hub, searchable at huggingface.co/datasets — over 300,000 public datasets as of 2026. Load any dataset in one line: from datasets import load_dataset; ds = load_dataset(‘stanfordnlp/imdb’)Each dataset ships with typed splits (train/validation/test), a features schema (text, image, audio, labels), and optional streaming for terabyte-scale files. Push your own data with ds.push_to_hub(‘your-username/your-dataset’) after running huggingface-cli login. A Hugging Face dataset is a versioned, structured data collection stored on the Hugging Face Hub and consumed through the datasets Python library.

Défiler vers le haut