Tools, frameworks, and models for local AI
22 resources found for "Llama"
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.
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).
Intel (Santa Clara, California, USA): founded in 1968, its 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.
| Headquarters | Santa Clara, California (USA) |
|---|---|
| Founded | 1968 |
| Employees | ~110,000 |
| Revenue | ~52 bn USD (2025) |
| Ownership | Public company (Nasdaq: INTC) |
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.
Meta's largest open model: closed-frontier quality but datacenter requirements. In practice it needs high-VRAM multi-GPU or the cloud.
Frontier-class open model with 131K-token context. Needs 48-80GB VRAM, a Mac with unified memory, or a multi-GPU setup.
The de-facto local workhorse: solid quality, 131K-token context and a huge GGUF quantization ecosystem. The standard choice for chat and assistants.
Small, fast model with up to 131K-token context. Ideal entry point for chat and summarization on modest hardware, great on phones and mini-PCs.
Il modello MoE grande della famiglia Llama 4 di Meta: 400B di parametri totali con 17B attivi (128 esperti). Contesto 1M di token, nativamente multimodale (testo+immagini), licenza comunitaria Meta. Richiede multi-GPU o macchine con memoria unificata 128GB+ anche quantizzato.
The compact MoE of the Llama 4 family: 17B active parameters out of 109B total (16 experts). Meta's community license allows free use under 700 million monthly users. One of the best entry points into the Llama 4 family.
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).
C++ implementation of LLMs for efficient inference on consumer CPUs and GPUs.
La workstation Apple di fascia alta: M5 Max con 18 core CPU, GPU fino a 40 core con Neural Accelerator e fino a 128GB di memoria unificata a 614 GB/s. Modelli fino a ~120B in 4 bit (e i MoE a bassa attivazione come gpt-oss-120b in tempo quasi reale), con MLX e llama.cpp. 10GbE di serie, Thunderbolt 5, fino a 8 display.
Open-source inference engine (Rust, YC S25) that compiles and tunes its kernels directly on the target hardware: Apple Silicon, NVIDIA, AMD or CPU. On open models it claims up to 2× the speed of llama.cpp. Also aimed at serving agents.
Meta Platforms (Menlo Park, California, USA): founded in 2004 as Facebook, it publishes the Llama family — the world's most widely used open-weight LLM line, from 1B to 405B parameters, under a commercial-use license. The Llama models in the catalog are the child resources.
| Headquarters | Menlo Park, California (USA) |
|---|---|
| Founded | 2004 |
| Employees | ~72,000 |
| Revenue | 201 bn USD (2025) |
| Ownership | Public company (Nasdaq: META) |
The first open-source MoE of OpenBMB's MiniCPM-V series: 35.2B total parameters, ~3B active per token, multi-image and video understanding with early visual-token compression (LLaVA-UHD v4). First open-source MoE with fluid on-device decoding — 13+ tok/s on 3GB of RAM on an iPhone 17 Pro Max — thanks to Lookahead Expert Scheduling (llama.cpp-omni), fine per-level quantization and DSpark speculative sampling. Day-0 support for Rockchip, Houmo, Intel, AMD and Arm; runs on Ollama, llama.cpp, vLLM and SGLang.
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.
The runtimes that run local models on your hardware: Ollama for simplicity, llama.cpp for portability, vLLM for production throughput. The engines in the catalog are the child resources.
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.
Community fine-tune of Qwen3.5-4B for 3D code generation: Three.js scenes, assets and interaction, also trained on the 3D-Scenes dataset. 4.5B parameters, Q4_K_M GGUF weights, runs locally with llama.cpp, Ollama, LM Studio and Jan.
A zero-dependency Python tool that recognizes and summarizes every file with a local LLM (Ollama, LM Studio or MLX-LM), fully offline. Detects type by content, extracts structure (CSV, email, scripts, logs) and reports files carrying instructions to the assistant. Works without a model (--no-llm). MIT license.
