Tools, frameworks, and models for local AI
20 resources found for "IA locale"
AMD: RDNA-architecture Radeon GPUs are the second consumer platform for local AI. Since ROCm 7, consumer cards (RX 7900 XTX, RX 9070 XT) are officially supported on Linux with no workarounds, and the Strix Halo APU brings up to 128GB of unified memory in a laptop. The GPUs and APUs 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.
Apple Inc. (Cupertino, USA): Apple Silicon chips and unified-memory Macs are a reference for local AI on consumer hardware. Apple products in the catalog are the child resources.
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.
NVIDIA's Rust CLI for AI-assisted Linux kernel development, focused on local AI: patch validation, builds, boot and tests, designed for backporting patches to older kernels. It supports local back-ends (Claude, OpenCode, Codex) or OpenAI-compatible servers. Apache 2.0, led by Andrea Righi.
Intel: Arc (Xe) GPUs are the third consumer platform for local AI, with 12-16GB of VRAM at entry prices. The maintained inference path is Vulkan (native Ollama, llama.cpp): the IPEX-LLM project was archived in January 2026. The GPUs in the catalog are the child resources.
La prima scheda Intel da prendere sul serio per l'IA locale: Xe2 Battlemage, 12GB GDDR6 a 456 GB/s, 190W. 12GB a prezzo d'ingresso: i modelli 8-14B in Q4 stanno interamente in VRAM. Il software è il punto debole: IPEX-LLM è stato archiviato a gennaio 2026, il percorso mantenuto oggi è Vulkan (Ollama nativo, llama.cpp) con ~40-42 tok/s su 8B Q4.
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.
Open-source tool that estimates local model inference speed across 250+ hardware configurations (GPU, Mac, CPU): measured estimates from community benchmarks or derived from similar setups. Use it to choose hardware before buying.
Il desktop compatto di Apple con il chip M6, primo processore Apple in 2nm: 12 core CPU, 12 core GPU con Neural Accelerator in ogni core e Neural Engine doppio da 16 core. 16GB di memoria unificata di serie (fino a 32GB, 170 GB/s), Wi-Fi 7, Thunderbolt 5. La piattaforma d'ingresso per IA locale: modelli fino a ~20B quantizzati a 4 bit girano interamente sul dispositivo, e la versione M5 Pro arriva a 64GB e 307 GB/s.
Open-source proxy that intercepts and displays the contents of HTTPS requests in plaintext: the tool to verify what actually goes over the network from your local AI setup.
Google's open-source (Apache 2.0) on-device GPU inference engine, successor to the TensorFlow Lite GPU delegate: tensor virtualization, stage-aware KV cache for prefill and decode, FP16/INT8/INT4. Backends: OpenCL (Android), Metal (Apple), WebGPU via Dawn (browser), OpenGL ES 3.1+. Integrates with LiteRT; in production on Chrome, YouTube, Photos and Meet.
GTK4/libadwaita app for GNOME to chat with local models via Ollama, in Rust under GPLv3: it handles Ollama setup, models, and loading local documents and images.
NVIDIA: CUDA-architecture GPUs are the de facto standard for local AI inference. VRAM determines which models run locally: 24GB (RTX 4090) covers ~30B quantized, 80GB (A100/H100) covers 70B+ models at full precision. The GPUs in the catalog are the child resources.
La workstation desktop NVIDIA per l'IA locale: superchip Grace Blackwell GB10 con 128GB di memoria unificata a 273 GB/s e circa 1 petaFLOP in FP4. CUDA completo, ~12 tok/s su modelli 27B a 4 bit, prefill veloce su contesti lunghi. Prezzo al lancio 3.999 USD, poi 4.699 USD per la carenza di memoria.
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.
Full ChatGPT-style web interface for Ollama and llama.cpp: multi-user accounts, organized conversations, document management for RAG, per-model system prompts, tools and plugins. Version 0.11.4 (September 2026) makes file metadata travel with the retrieved chunks and runs hybrid search natively in the database: large knowledge bases are much faster. The simplest way to turn your local AI into an everyday product.
Single-board computer with an 8-core ARM Cortex-A76 CPU and 2–16 GB of RAM: the reference for learning and running small local AI models in CPU inference.
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.
