Migliori LLM locali del 2026: usali con Ollama o LM Studio - Risposte Informatiche
claude llama
| Source: Mastodon | Original article
A new guide from the Italian tech forum Risposte Informatiche has mapped the most compelling large language models (LLMs) that can run locally in 2026, pairing each model with the two dominant deployment stacks – Ollama and LM Studio. The list, published six hours ago, goes beyond a simple catalog; it supplies concrete RAM and VRAM thresholds, quantisation tips and compatibility notes for Apple’s Metal Performance Shaders (MPS) and the emerging MLX framework.
The timing is significant because the surge in on‑device AI, spurred by recent hardware milestones such as the iPhone 17 Pro’s ability to host a 400‑billion‑parameter model, is pushing developers and power users toward self‑hosted alternatives to cloud services like ChatGPT or Claude. Ollama remains the quickest route for terminal‑oriented workflows and API integration, while LM Studio’s graphical interface and built‑in model browser appeal to non‑technical users. By spelling out which models fit a 8 GB‑RAM laptop versus a 24 GB‑VRAM workstation, the guide lowers the barrier to entry and helps avoid the performance pitfalls highlighted in earlier optimisation pieces on quantisation and MPS acceleration.
As we reported two weeks ago in “Ollama vs LM Studio vs GPT‑4All: Local LLM Comparison 2026,” the ecosystem is fragmenting into three clear niches: lightweight inference, developer‑centric scripting and full‑stack GUI tools. This fresh ranking confirms that fragmentation is stabilising around a core set of models – Gemma 3 1B, Qwen 3 0.6B, DeepSeek‑V3.2‑exp 7B and the open‑source LLaMA‑4 8B – each with a sweet spot in memory usage and reasoning capability.
What to watch next is the rollout of hardware‑specific kernels that promise sub‑second latency on consumer GPUs, and the upcoming open‑source quantisation libraries that could shrink the 8 GB‑VRAM ceiling further. If those advances materialise, the line between cloud‑grade and desktop AI will blur even more, making the guide’s hardware‑first approach a crucial reference for anyone looking to keep AI on‑premises in 2026 and beyond.
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